Art Photography Prices - Erasmus University … Stournaras Master...Art Photography Prices In the...

Art Photography Prices In the auction market

Nikolas Stournaras 313168

2009

Supervisor: F.R.R Vermeylen

Erasmus University Rotterdam

Faculty of History and Arts

Master Program Cultural Economics and Cultural Entrepreneurship

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1. Introduction ................................................................................................ 4

2. Literature review ........................................................................................ 7

2.1 The art market ........................................................................................ 7

2.2 The primary market ................................................................................ 8

2.3 The secondary market ............................................................................ 9

2.4 Auction houses ..................................................................................... 10

2.5 The market for visual art ....................................................................... 12

2.6 Art prices .............................................................................................. 13

2.6.1 Previous studies on art prices ........................................................ 13

2.6.2 Artwork related price determinants ................................................ 16

2.6.2.1 Size of artwork ............................................................................ 16

2.6.2.2 Medium used – materials ............................................................ 17

2.6.2.3 Labour time ................................................................................. 17

2.6.2.4 Date of creation – Age of artwork ............................................... 18

2.6.2.5 Provenance ................................................................................. 18

2.6.2.6 Signature - authenticity ............................................................... 18

2.6.3 Artist related price determinants .................................................... 19

2.6.3.1 Reputation .................................................................................. 19

2.6.3.2 Age ............................................................................................. 20

2.6.3.3 Gender ........................................................................................ 20

2.6.3.4 Education – Influences ................................................................ 21

2.6.3.5 Death effect ................................................................................ 21

2.6.4 External price determinants ........................................................... 22

2.6.4.1 Expert opinion – estimation ......................................................... 22

2.6.4.2 Economic factors – Comparison with other markets ................... 23

3. A historical summary .............................................................................. 25

3.1 Introduction ........................................................................................... 25

3.2 The foundation of physics and chemistry ............................................. 25

3.3 Photography did not happen accidentally ............................................. 26

3.4 Photography as fine art and the different genres .................................. 27

3.5 Photography museums ........................................................................ 38

4. Data ........................................................................................................... 42

4.1 Sample overview .................................................................................. 42

4.2 Artists ................................................................................................... 43

4.2.1 Andreas Gursky ............................................................................. 44

4.2.2 Bernd & Hilla Becher ..................................................................... 45

4.2.3 Candida Höfer ................................................................................ 46

4.2.4 Cindy Sherman .............................................................................. 46

4.2.5 Hiroshi Sugimoto ........................................................................... 47

4.2.6 James Welling ............................................................................... 48

4.2.7 Jeff Wall ......................................................................................... 48

4.2.8 Philip-Lorca diCorcia ...................................................................... 49

4.2.9 Rineke Dijkstra ............................................................................... 50

4.2.10 Thomas Demand ......................................................................... 51

4.2.11 Thomas Ruff ................................................................................ 52

4.2.12 Thomas Struth ............................................................................. 53

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4.3 Data description ................................................................................... 55

4.3.1 Comparison between fine art categories........................................ 55

4.3.2 Descriptive statistics ...................................................................... 56

5. Methodology ............................................................................................. 68

5.1 Introduction ........................................................................................... 68

5.2 Variables .............................................................................................. 68

5.2.1 Size ................................................................................................ 68

5.2.2 Type of print ................................................................................... 68

5.2.3 Year of artwork .............................................................................. 69

5.2.4 Year of sale .................................................................................... 69

5.2.5 Place of sale .................................................................................. 69

5.2.6 Auction house ................................................................................ 69

5.2.7 Price............................................................................................... 69

5.3 Hypotheses .......................................................................................... 70

5.3.1 Hypotheses testing ........................................................................ 71

6. Analysis .................................................................................................... 74

6.1 Introduction ........................................................................................... 74

6.2 Model results ........................................................................................ 74

6.3 Conclusion............................................................................................ 81

6.3.1 Future research ............................................................................. 82

Bibliography & References ......................................................................... 84

Appendix 1 ................................................................................................... 87

Appendix 2 ................................................................................................. 101

Appendix 3 ................................................................................................. 115

Appendix 4 ................................................................................................. 121

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1. Introduction

The exploration of the art market is a real challenge. It‟s as fascinating as

being on a coastline, because it is the place where two different worlds meet. In this

case the art world meets the financial world. The art market is one of the main

reasons why art has flourished the past few centuries. The socioeconomic history,

the shaping of societies, trends and beliefs and also science allowed art to evolve

and create artists whose art legacies have become and will continue to be symbols of

human civilisation. Cultural economics has a potential interest in the art market as

well as other cultural fields. Cultural economists walk the coasts where culture meets

economy.

For people outside the art world, the art market has been almost identified

with the market for paintings and perhaps sculpture, which makes sense because of

the international popularity of certain artists, education on art history in most schools

etc. Of course painting and sculpture have a history of thousands of years, while

other media are relatively new, like for example photography which will celebrate 200

years of official history in 2039. Photography, mainly because of its vast commercial

use, has been a target of heavy criticism concerning its artistic validity from the very

beginning. Walter Benjamin‟s criticism on certain art styles such as abstract

photography and futurism in photography is a prime example.1 It has taken a lot of

photographic work by the people who are called today masters of photography as

well as argumentation from experts to mature the idea that a mechanised medium

can create artistically important artworks. Today the art market has accepted

photography as a fine art medium and photographs are sold in galleries and auction

houses. This is a sign that art photography of various periods from the last 150 years

is being considered more and more as an art investment by buyers.

The study of the art market is interesting as it is difficult. People involved in

the art market are not willing to share information, especially when it comes to

economic terms. Artists show great reluctance with anything that has to do with the

word “price”, as they are more interested in the intangible nature of their art rather

than the pecuniary part. Dealers are also reluctant in talking about figures. Olaf

Velthuis in his book “Talking Prices”2 explains his quest in interviewing gallery

owners, art dealers and other agents in the art market and their apparent distrust

1 Marien, M.W., 1997. Photography and its critics. Cambridge, UK. Cambridge University Press.

2 Velthuis, O. 2005. Talking Prices. New Jersey, Princeton University Press.

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against economists. This is a relationship that has to be structured and established

on common grounds which inevitably takes time.

This thesis‟s goal is to confirm or reject whether photography should be

examined separately from the visual arts such as painting or even reproducible

artworks such as prints and to explore this segment of the art market from a bird‟s

eye view. Does photography deserve to be considered a special category among

other visual art categories or does it follow the exact same paths in matters of price

formation and sales? My personal interest and background in photography were the

determining factors in deciding my thesis topic. Even though there is an abundance

of literature concerning visual arts economics in general, the mentioning of

photography specifically is somewhat scarce or even non-existent. This fact was my

motivation to make a study that could fill this gap and investigate the possible

differences between photography and the visual arts in general in terms of their

presence in the art market and especially in the formulation of the price. The fact that

it is a non investigated field made it even more interesting to start a research. Of

course soon the limitations of describing a whole market on photography in a short

period of time like the one of a master thesis drove me to the studying of a particular

part of that market where useful data could be obtained. The lack of appropriate data

concerning the primary market enables the use of auction data which is published

and easier accessible, in this case the auction market for contemporary photographs.

Auction houses are the ones that reveal the highest willingness to pay on specific

items, and photography is gradually becoming part of this process. Especially after

the year 2000 some constantly increasing top-sales figures are rather striking,

showing that the ceiling has not been reached yet.

The analysis of this thesis makes use of data concerning 12 contemporary art

photographers, who are also presented in a brief manner along with some historical

background of art photography and photographic museums; afterwards it presents

some interesting descriptive statistics concerning their work, where the most

important photography auctions take place and by which auction houses; it then goes

on to identify the possible predictors of price with the use of linear regression models.

Literature has already been composed by several studies exploring the determinants

of prices mostly for paintings. Pesando3 is the first who constructs an art price index

for repeat sales of prints, which means reproducible artworks like silk screen prints,

lithographs, woodcuts etc. Photography also falls into this category even though it is

not mentioned in Pesando‟s study. Photographs are also printed out in editions of a

3 Pesando, J. 1993. Art as an investment: The market for modern prints. The American Economic

Review vol.83, pg. 1075.

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number of exemplars or can also be unique prints and therefore may behave as

unique as well as reproducible artworks.

The structure of this thesis goes as follows: A literature review presents in

short several theoretical topics that represent the foundation on which the entire

study is built on, namely the art market and its segments and afterwards the auction

houses and art price theory. Several determinants are described and connected to

various references in the literature of cultural economics. Then the literature review

concludes with a presentation of studies that have been carried out concerning art

price index construction, art as investment which is closely related and a distinction

of the various methods that have been used to analyse databases.

In the third chapter there is a concise history of photography to allow the

reader to acquire a general view of what is photography and how it has evolved until

the present day mainly from the aesthetic point of view with some important

technological breakthroughs. Some of the most important names in the history of the

medium are mentioned alongside with well known photographs of them. This was

meant to give a written but also visual timeline to the reader and describe the

influences these people had on the contemporary photographers of the sample. The

chapter ends with a historical overview of photographic museums from the 19th

century until nowadays.

In the fourth chapter the data used in the analysis is presented; first of all, the

sample of the 12 contemporary photographers is described with a short biography

and one picture showing the artwork that has achieved the highest price (or one of

the highest) for each one. The data description follows with frequency tables for the

number of artworks sold per artist and the amount of turnover raised, as well as

diagrams showing the market shares of auction houses and cities where the most

important auctions take place.

In the methodology part the variables and hypotheses to be tested are shown,

along with the regression model that is used in the analysis part. The analysis shows

the coefficient tables and subsequently there is a discussion of the results. The

appendix shows a variety of information per artist that could not be embodied in the

main text.

To summarize, the present thesis focuses on the auction sales of art

photography by 12 contemporary artists in an attempt to identify the most important

price determinants of photographic artworks.

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2. Literature review

In this first chapter I will present the theoretical background of my thesis as

well as some previous studies that have been carried out for price formation and

price indexing of artworks before.

2.1 The art market

Art market literature usually divides the art market into primary and

secondary. Before analysing each of them it is useful to have in mind three elements

that help making classifications. These are the “who”, “what”, “where”. So far

interpretations have tried to make distinctions based on all these three elements. In

1994, a suggestion was made by Singer and Lynch4 to divide the art market into

three subcategories, namely the primary, secondary and tertiary market using the

following differentiation: in the primary market artists sell their works to dealers and

collectors, in the secondary dealers sell to collectors and in the tertiary market

collectors and dealers recycle artworks that had been previously transacted in the

secondary market. This approach although not widely accepted by others in the field

is worth mentioning. One initial observation is the exclusion of auction houses into a

separate submarket, the tertiary market.

However, the approach is probably more focusing on “who” deals with whom

and not “where” the deal takes place. The artist>dealer>collector flow is the one that

describes the sequence of participants in the primary and secondary market, while

the tertiary market simply adds another direction between the dealers and collectors

(artists>dealers<=>collectors) who continue to transact through the auction houses

without the participation of artists – until very recently as mentioned further on. Other

economists have commented on this classification as “unnecessarily complicated”5

especially since in later years there has been a blending between these categories

as dealers participate on both the primary and secondary market and auction houses

are extending their reach into the primary market as well. This doesn‟t leave much

space for a clear segmentation of the market based on all above mentioned

elements. Since “who” and “where” are out of focus the only perhaps element left to

4 Singer, L., Lynch, G. 1994. Public choice in the tertiary art market. The Journal of cultural economics

vol.18: 199-216. 5 Heilbrun and Gray are commenting on Singer’s article of 1994 in the Journal of Cultural Economics

No.18: 199-216. It seems that in the general bibliography the tertiary market segmentation is not

adopted by other economists.

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make a clear cut distinction is the “what”, in other words what is dealt in a sense of

whether it is thrown in the market for the first time and what is the time lapse between

its creation and its transaction (the dealing of antiquities for instance, which are

considered secondary market items).

Photography in the art market exists for almost a hundred years since it was

first introduced into the gallery system with Stieglitz‟s Gallery 291. (See Historical

summary chapter 3). That generation of photographers and the next ones spent their

lives advocating for photography‟s rightful position among the fine arts. Today the

fruits of their arguments have provided us with a richer capital of art photography

worldwide and a continuously growing art photography market. Descriptive statistics

from the sample data will confirm this in the Data section (chapter 4) of this thesis.

2.2 The primary market

The main distinction that leaves no grounds for misinterpretation is that the

primary market consists of all transactions of original artworks that are sold for the

first time6 by the artists themselves or dealers who have an exclusive relationship

with the artist. There is usually little or no information on the buyer‟s side which

includes a great amount of risk for the potential buyer. In this case the artist may be

unknown and presenting his work for the first time in the market or it may be the case

of more established artists presenting their latest work. The studio or the gallery is

usually the place where the works are shown for the first time and also the place

where the first potential buyers are going to visit. Artists typically provide the creative

work while gallery owners or dealers provide the market knowledge. Based on

previous sales of the same artist or the prices of similar works from other artists of

the same genre, a current “feel of the market” and perhaps other kinds of information,

a dealer is setting a price for each artwork or group of artworks which is most likely to

sell. In cases of new unknown artists prices are kept low and if sales are

encouraging, the next exhibition is almost certain to involve higher prices. For this

reason galleries are traditionally connected with the term of primary market. However

there is a tendency during the recent past years that galleries are also expanding

their business into the secondary market, while artists themselves attempt to bypass

the dealers and sell their artworks though auctions themselves. One recent example

of 2008 was Damien Hirst selling original artworks of his directly through auction

6 Heilbrun, J. & Ch. M. Gray. 2001. The Economics of Art and Culture, second edition. Cambridge,

New York: Cambridge University Press.

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without the contribution of other intermediaries. If the primary market could be

characterised with a market structure it would most probably be a monopolistic

competition. Heilbrun takes performing arts as an example of monopolistic

competition but this example could very well be extended to the primary visual art

market and especially to galleries as there are enough suppliers that offer physically

similar products but from the demand‟s point of view can acquire different or even

unique values (preferences on particular artists or styles). That is why galleries tend

to make exclusive deals with artists so that they can be the ones to have a kind of

monopoly over their work. Buyers that are searching for new artworks visit exhibitions

acquire more information on the artworks on display and begin forming their own

preferences.

Olav Velthuis7 in his book titled “Talking prices makes a wonderful study on

the Dutch primary art market and presents the most important predictors one should

bear in mind in order to make an estimate of a contemporary work of art that enters

the market for the first time. His findings indicate that the size of the artwork should

be the first to take into consideration, as well as the technique that has been used

and the price that the artist may have sold to museums. Apart from those the age of

artist and his place of residence appear to be the most significant factors while the

characteristics of the galleries seem to hardly influence the price.

The art photography primary market is perhaps the most difficult to measure

and identify, especially historically, as the percentage of images sold by galleries is

relatively quite low in comparison to painting. There is unfortunately no data, which I

could acquire, describing this percentage; however the task of tracking down

galleries that buy art photographs exclusively or along with other artworks is both

challenging and interesting. Especially since during the last decade photography is

clearly rising in preference by art collectors as we shall see further on. The fact that

this increase is valid for contemporary art in the secondary market declares that the

primary market allowed these contemporary artists to rise to fame. Today‟s famous

artists such as the ones in the sample of the analysis are each represented by

several galleries from different parts of the globe, especially in cities like New York or

London or their home countries.

2.3 The secondary market

The secondary market involves all transactions of existing artworks that have

been sold at least once before or works of artists that have passed away and

7 Velthuis, O. 2005. Talking Prices. New Jersey, Princeton University Press.

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therefore their work is determined. In the secondary market, information is much

more available to all participants due to the reputation of the artist and as a result the

purchase of a recognised artwork entails less risk. In other words, the economic

value of an artwork has to be established to a certain level before it can be

characterised as an investment option8. Be that as it may, this doesn‟t mean that art

as an investment entails less risk than a financial investment especially because of

liquidity reasons, however this is a discussion that is not further developed in the

course of this thesis. This is why the secondary market attracts more people who

want to invest in art with a lower risk rate, and are therefore willing to pay higher

amounts of money in order to purchase certain artworks. Auction houses have for

this reason become the main suppliers of such artworks. It is not exaggerating to say

that auctions are perhaps the only place to examine the levels of willingness to pay

for art worldwide. They are perhaps the institutions that maintain the “Veblen effect”

more than any other institution active in the art market. The Veblen effect is the

tendency to derive quality of an artwork mainly by its sale price9.

2.4 Auction houses

McAfee and McMillan10 give a descriptive definition stating that “an auction is

a market institution with an explicit set of rules determining resource allocation and

prices on the basis of bids from the market participants.” The most famous type of

auction which literally dominates the auction market is the English type where prices

ascend in open bidding11. The artwork is displayed in front of the potential buyers and

the bidding starts low at the so called reserve price determined by the auction house

and the price begins to escalate until it reaches its highest bidding point where it is

“hammered down” an indication that it is sold at that price (hammer price). Auction

theory can go deeper into details on how auctions work in practice, about whether a

hammered down item is eventually sold or not, but for the purpose of this research it

is not necessary. In the analysis (Chapter 6), the data includes the hammer price as

the actual price of the artwork at that very moment.

8 Velthuis, O. 2003b. Visual Arts. In: Towse. R. (ed.) A handbook of cultural economics. Edward Elgar,

470-475. 9 The Veblen effect is also mentioned by Rengers – Velthuis (2002) and they also refer to Leibenstein’s

article of 1950: Bandwagon, Snob and Veblen effects in the Theory of consumers’ demand. Quarterly

Journal of Economics vol:64 pg.183-207. 10

Heilbrun, J. & Ch. M. Gray. 2001. The Economics of Art and Culture, second edition. Cambridge,

New York: Cambridge University Press. 11

Ashenfelter, O. 2003. Art auctions. In: Towse. R. (ed.) A handbook of cultural economics. Edward

Elgar, 32-39.

11

The market structure for auctions is clearly an oligopoly and more specifically

a duopoly, since in 200712 “Christie‟s generated the largest share of global Fine Art

revenue with 38.7%, ahead of its only real competitor, Sotheby‟s, with 36%. Together

they account for almost the entire volume of global auction revenue since Phillips De

Pury, in third place accounts for only 2.6% followed by Poly International Auction

(1.8%), China Guardian (1%) and Artcurial (0.9%).” This duopoly appears to be quite

strong and is based on the reputation of these two auction houses, the building of

which has worked as a restrictive barrier for the entry of new competitive enterprises

at least at that level13. In other words the cost of creating a reputation as long and

strong as Christie‟s and Sotheby‟s have, is considered impossible for smaller auction

houses. This is also reflected on the prices of the artworks that every auction house

can achieve. The reputation is inevitably the factor that attracts both important sellers

but also important buyers. It is also significant to mention that geographically there is

also a clustering of revenue mainly in New York, USA and London, UK. The

revenues of auctions held in the United States reaches 43% of global auction

revenues and London comes in second place with 30%. Even though the two great

auction houses have departments in several cities across the United States and

abroad, New York and London seem to be the Mecca and Medina for auctions. If we

add the amounts of annual turnover that the major auction houses have in relation to

the auction markets‟ as a whole, this can only bring us to the conclusion that the

auction market is a winner-take-all type of market.

The winner-take-all market14 is a market that functions in an opposite way

than normal markets for various reasons. In normal markets the performance is

measured with efficiency and the product or service is rewarded according to that

efficiency. In certain domains the difference in this reward is relatively low (manual

labour) however in other fields of employment it can be disproportionate. The fact

that people are willing to pay these higher figures in order to obtain a product that

they could possibly find at a lower cost is what makes the market become winner-

takes-all. Superstars are the ones who take most credit through the media and their

reputation grows in a continuous manner. The information economy contributes to

this growth. Superstars get more and more attention through the mass media and

this is reflected in their rewards. The movie industry is such an example. Some

Hollywood actors are paid significantly higher than others who could perform almost

12

Artprice (2008), 2007 Art market trends, Artprice.com.

13

Ashenfelter, O. 2003. Art auctions. In: Towse. R. (ed.) A handbook of cultural economics. Edward

Elgar, 32-39. 14

Source : The Economist (http://www.economist.com/research/Economics/alphabetic.cfm?letter=W)

12

as well for the same role. If one imagines the distribution of fees in this case the

upper observations would be extremely high while the median would appear quite

low in comparison to the mean. In a normal economy this situation would attract

competition and lower the amount of fees of the overpaid actors and would therefore

make the distribution of fees less skewed. However the winner-takes-all market

allows this skewness to be profound and also creates a tendency for it to increase.

The auction market therefore falls into this category. The auctioneers will very

unlikely put anything on for sale if they are not almost certain that it will be within the

boundaries of their estimates or hopefully above them. Especially large auction

houses like Christie‟s and Sotheby‟s base their reputation on the fact that they will

have a good percentage of artworks sold within the estimates and of course these

prices will be significantly higher than in other auction houses.

Prices of a single artist show great variance when estimated by auctioneers15.

Of course well known artists have higher price ranges from relatively unknown ones

but the price formulation is much more complex than that.

Photography in auctions seems to have a relatively short history. The early

90s is the time when the art market showed a particular blossom and brought art

photography into the salerooms. The first years seemed more experimental judging

from figures (presented in the Data chapter 4) but at the turning of the century the

increasing interest of the auction market is apparent.

2.5 The market for visual art

The visual art market is an ideal example to portray the art market, as it

involves a great percentage of the art market in general. It is not by chance that most

people immediately combine the art market with the market for paintings. As

expected the visual art market is also divided into primary and secondary. According

to Velthuis16 some artists sell their artworks directly from their studios or maintain

cooperation with intermediaries such as commercial art galleries or art consultants. It

is also a fact that the vast majority of visual artists cannot make ends meet just by

selling their work in the primary and only a small percentage from those who do, are

able to trade their work in the secondary market. In other words only a small number

of artworks produced by living artists are traded by auction houses which are the

case of this research. This means that a great number of artworks will not exceed the

15

Moulin, R. 1967. revised and translated in 1987. The French Art Market: a sociological view. New

Brunswick, Rutgers University Press. 16

Velthuis, O. 2003a. Symbolic meanings of prices: Constructing the value of contemporary art in

Amsterdam and New York galleries. Theory and Society vol:32 pg181-215.

13

boundaries of the primary market and thus not appear in the market ever again.

However, the artworks that do come back on the market reveal that they have

actually passed a certain process, a crash test that has given them the properties to

be handled by secondary market agents such as auctions. And there are several

other agents in the arts sector that assist in the formation of such properties. These

are called cultural institutions. Cultural institutions can be art schools, exhibition halls,

museums, dedicated art magazines etc. Their role is quite essential as they inform

and shape the taste of a large part of the demand for artworks whether that is a

collector or a dealer. They are the gatekeepers that allocate the demand into this

selection of artists. The selection of course is a process of judgement from expert

knowledge on the art form, previous artworks that have been successful on the

market etc. By selecting this small group of artists, cultural institutions reduce the

information and search costs for all agents in the market, especially art collectors,

thus enhancing the overall value and credibility of the artists and their work. This

process creates the necessary ground for the creation of superstar effects and drives

the market into winner-takes-all phenomena.

2.6 Art prices

2.6.1 Previous studies on art prices

In this section there will be a short review of studies carried out so far that

deal with price indices, determinants of prices and art as investment using mainly

auction data. In terms of method Worthington and Higgs17 mention three methods

that have been used to analyse data; The first is the arithmetic or “naïve” art index

method, the second is the repeat sales method which follows the sales of specific

artworks that have been sold various times and the third is the hedonic price

regression. The oldest and most concrete database that many researchers have

been using is the one of Reitlinger18 (1961, 1970) and Mayer19 (1971). Anderson20

(1974) investigates old master paintings (18th and 19th century, Impressionists and

20th century artists) for the period of 1780 to 1970 using the Reitlinger data. His

findings point out size, year of sale and reputation as the most significant

17

Worthington, A. Higgs, H. 2006. A note on financial risk, return and asset pricing in Australian

modern and contemporary art. Journal of cultural Economics vol:30 73-84. 18

Mentioned in various studies are two books of Reitlinger:

1) Reitlinger, G. 1961. The economics of taste: The rise and fall of the picture market, 1760-1960.

Holt, Reinhart and Winston, New York.

2) Reitlinger, G. 1970. The economics of taste: The art market in the 60s. Barrie and Jenkins Ltd.,

London. 19

Mayer, E. 1971. International Auction records Vol.5. Mayer and Archer Fields, New York. 20

Anderson, R. 1974. Paintings as an investment. EconomicInquiry vol:3 13-26.

14

determinants. Stein21 (1977) also carried out a similar study and computed a

consumption return rate for paintings. Baumol22 (1986) also used the same database

to conclude the lack of an equilibrium in the art market and the randomness and

unpredictability of the prices. Frey and Pommerehne (1989) support Baumol‟s

findings using an even larger sample with more countries and including transaction

costs. All four researchers come to the conclusion that art investment returns are

lower than other investments. Buelens and Ginsbergh23 (1993) study the same data

as Baumol but trying to stratify the data to explain his results by analysing different

periods of sales especially those of political and economic unrest. They use both the

repeat sales and the general regression model for all data (like Anderson) and come

to the conclusion that by using all the data they are able to produce more significant

results but with lower R²s. Goetzmann24 (1993) uses also the Reitlinger data for

repeat sales regression to come to his conclusion that stock market and the art

market are highly correlated. Pesando25 (1993) uses data from Gordon‟s Print Price

Annual to estimate a repeat-sales regression to make a similar study for prints in

comparison to stocks and bonds and makes two indices for Picasso prints and others

(Chagall, Miro etc). His findings are in support of previous studies that art does not

compare favourably with financial assets and that the art market is overall inefficient.

Agnello and Pierce26 (1996) created an index of 66 leading American artists and

came to the conclusion that by investing at certain subjects (figure paintings, avant-

garde, still life etc) and high priced artworks, the buyers can achieve greater returns

and they compare it with Buelens and Ginsburgh‟s findings that certain paintings are

lowering the returns of the whole sample. Czusack27 (1997) examines Picasso

paintings sales to check the influence of several predictors and comes to the

conclusion that signature and provenance do not have an impact unlike as expected.

Candela and Scorcu28 (2001) create price indices for the secondary market of

21

Stein, J. 1977. The monetary appreciation of paintings. Journal of Political Economy vol:85 1021-

1035. 22

Baumol, W. 1986. Unnatural Value: or Art Investment as a Floating Crap Game. The American

Economic Review vol.58, pg. 933-942. 23

Buelens, N. , Ginsbergh, V. 1993. Revisiting Baumol’s Art as a floating Crap Game. American

Economic Review vol:37 1351-1371. 24

Goetzmann, W. 1993. Accounting for taste: Art and the Financial Markets Over Three centuries. The

American Economic Review vol.83 pg.1370. 25

Pesando, J. 1993. Art as an investment: The market for modern prints. The American Economic

Review vol.83, pg. 1075. 26

Agnello, R. Pierce, R. 1996. Financial returns, Price determinants and Genre effects in American art

investment. Journal of cultural Economics vol.20 359-383. 27

Czusack, C. 1997. Picasso paintings at auction, 1963-1994. Journal of cultural Economics vol:21

229-247. 28

Candela, G. Scorcu, A. 2001. In Search of stylized facts on Art market prices: Evidence from the

secondary market for prints and drawings in Italy. Journal of cultural Economics vol: 25 219-231.

15

drawings and prints and conclude that the secondary market price index is influenced

by the auction price index and therefore they suggest that auction prices should be

considered a benchmark for other institutions in the art market. Drawings,

watercolours etc are more valued than prints, engravings etc. The same researchers

in another research in 2004 propose a price index adjusted for the quality of the

paintings using the ratio of the average market price and the average pre-sale

estimate for paintings at auction. According to their findings this method provides

results with less volatility than quality unadjusted and hedonic price indices. Valsan29

(2002) uses the hedonic regression model to investigate the relation between market

value and nationality by comparing Canadian and American painters. Worthington

and Higgs30 (2006) explore the price indices of 50 Australian artists in a period of 30

years and examined the influence of the Australian stock market to the art market.

Hutter et al31 (2007) compare quoted dealer prices of 100 leading visual artists from

1970 to 2004 with auction price results for works by the same artists.

A work of art can be characterised as a commodity by its properties32. First of

all are the physical properties such as the size, the materials used, the labour time

that was needed to be completed, date of creation and the name of the creator.

However there is also the date and the place of sale that can alter the price of the

same artwork. History of art has shown that through the last centuries there were

shifts from some properties to others in terms of significance. To put it in a more

mathematical form, the variables (properties) in the function (price) showed different

factors over time. In the Renaissance for instance, art had a more practical and

everyday use and so the price for each artwork was usually fixed and dependent on

the cost of production i.e. materials and labour cost. From the mid seventeenth

century on, the choice of subject played the major role as historical paintings were

considered more highly than ordinary landscapes or portraits. Later on, the signature

of the artist played a greater role in determining the price of artwork by the same

artist because it acted as a trademark. It was at some point impossible to price an

artwork of an artist if it wasn‟t signed. Apart from the physical attributes of each

artwork there are also other factors that can influence the price such as the place and

29

Valsan, C. 2002. Canadian versus American Art: What pays off and why. Journal of cultural

Economics vol: 26 203-216. 30



Hutter, M. et al 2007. Two games in town: a comparison of dealer and auction prices in

contemporary visual arts markets. 32

Sagot-Duvauroux, D. 2003. Art prices. In: Towse. R. (ed.) A handbook of cultural economics.

Edward Elgar, 57-68.

16

the date of sale. The price of every commodity is subject to macroeconomic variables

like inflation that change the price as time advances. In a globalised market as today

the currency exchange rates also play a role in defining prices or artworks in different

parts of the world. This is why it is essential to make the proper deflation techniques

and the use of single currency in order to make comparisons on art prices between

years. In general it is plausible to divide the determinants of prices into three major

categories, as previous studies have done already33. These are the intrinsic

characteristics of the artwork itself, artist-related factors and external factors.

2.6.2 Artwork related price determinants

2.6.2.1 Size of artwork

The dimensions of an artwork are agreed by almost all researchers to be one

of the most significant determinants of the price of the artwork34 and is therefore used

in all studies as a predictor of price. However some researchers argue that other

factors may intervene in the amount of impact that size can have on price eventually.

For instance Czusack35 identifies this in terms of the buyer as collectors who

represent a great portion of demand are thought to prefer smaller artworks that they

can hang on their walls while large artworks usually are preferred by museums. The

size – price function follows a concave curve meaning that larger artworks are not

necessarily cheaper but the marginal price drops as size grows beyond a certain

surface limit. Other researchers such as Rengers and Velthuis36 mention that the fact

size is used as a determinant is an institutionalised rule of pricing adopted by

galleries and dealers. Today‟s examples of expensive small sized artworks are few

and are usually because of the dominating effects of other determinants such as

scarcity or reputation of the artist.

33

Refering to Sagot-Duvauroux, D. 2003. Art prices. In: Towse. R. (ed.) A handbook of cultural

economics. Edward Elgar, 57-68 and Rengers, M. Velthuis, O. 2002. Determinants of prices of

contemporary art in Dutch galleries 1992-1998. The Journal of cultural economics vol.26: 1-28. 34

Frey, B., Pommerehne, W. 1989. Muses and markets. Explorations in the economics of the arts.

Basil Blackwell, Oxford, UK. 35


229-247. 36

Rengers and Velthuis actually quote this from one of their sources i.e Hans Abbing (1989) Een

economie van de kunsten. Beschouwingen over kunst en kunstbeleid. Historische Uitgeverij,

Groningen.

17

2.6.2.2 Medium used – materials

According to Sagot Duvauroux37, the materials used for the creation of the

artwork, influence its price. Rengers and Velthuis38 also agree on this with the results

of their study on Dutch galleries mentioning that oil works are priced higher than

watercolour and canvas is also priced higher than paper. They also mention the

examples of reproducible artworks that are produced in editions such as lithographs

and silk screen prints which are less costly to produce per unit than a unique work.

The case of photography could not be different. Photographers usually print their

works in editions of 50 or 100 exemplars or even less. This depends on the method

used and how mechanised the whole process can be. For instance the use of a

darkroom by the artist himself or the use of a professional lab creates differences in

matters of total cost and labour time discussed below. Also, colour photographs for

instance printed in a darkroom in the conventional way (film exposure on paper) is

much more expensive than doing so in a professional photo-lab. Different materials

and methods can increase the price per print but do not necessarily indicate a higher

price of sale. This has probably more to do with lack of information on behalf of both

galleries and buyers as to the true costs of taking a picture and printing it which of

course can vary greatly in actual numbers.

2.6.2.3 Labour time

Labour time and therefore labour costs is another factor that is usually

combined with the technique or method used. This factor has not been taken into

much consideration by researchers and is probably combined with technique,

material and medium to indicate its impact on the price function. In the past this

determining factor used to play a significant role in calculating the reward of the artist,

as artists were considered more like technicians and decorators rather than artists

as we consider them today. In the modern era, the process of creating art is not

taken into account and the judgement is based mostly on the aesthetics of the result.

37


Edward Elgar, 57-68. 38

Rengers, M. Velthuis, O. 2002. Determinants of prices of contemporary art in Dutch galleries 1992-

1998. The Journal of cultural economics vol.26: 1-28.

18

Of course it is implausible to define a standard labour time and cost for each

technique but this variable should probably be examined more thoroughly.

2.6.2.4 Date of creation – Age of artwork

The age of the artwork is also important in determining the price because it

places it in a genre, defines its scarcity and perhaps historical importance. As

Hutter39 explains, studies over the impact of the age of artworks has been carried

out by Galenson and Weinberg in 2000 and Landes also in 2000. Their results

showed significant positive effects of the age of artworks40. In the case of

photography, the various techniques that were used in the 19th century and the

beginning of the 20th century give an approximate estimation of the period the

pictures were taken, as the technological changes enabled photographers to shift to

the new and more efficient technique. Of course, as the distribution and information

channels were less developed back then some people in countries far from the

United States or western Europe continued to use materials and techniques of older

technology because of lack of information, supply or cost.

2.6.2.5 Provenance

Provenance indicates the origin of the artwork and perhaps its previous

owners. This acts as a positive bias for potential buyers especially in the case of

auction houses. Certain buyers feel more secure to acquire an artwork of prestigious

provenance so that they can have an additional guarantee that their purchase will not

lose its value or historical importance in due course.

2.6.2.6 Signature - authenticity

Signature and authenticity are quite correlated. It is usually the existence of a

signature that verifies the identity of the artist and therefore allows for his

characteristics (fame, technical skill etc) to influence the value of the certain artwork.

There are also cases when unsigned artworks are attributed to certain artists

because of similar characteristics which are identified by experts. Nevertheless, the

39

Hutter, M. et al 2007. Two games in town: a comparison of dealer and auction prices in

contemporary visual arts markets. 40

Hutter also mentions another study of Singer (1990) and that these studies by Galenson and Landes

have given further support to Singer’s hypothesis of “artist capital stock”.

19

existence of a signature adds to the value of the painting and gives the buyer or

consumer a sense of prestige41.

2.6.3 Artist related price determinants

2.6.3.1 Reputation

The artist‟s reputation is of profound importance for the estimation of the

price. It summarises the value of his or her creations and is perhaps the most

important reason why a potential buyer would invest in an artwork and place the

highest bid at an auction. Reputation should not be confused with talent in this

manner as it is shaped by expert opinion. According to Bonus and Ronte42

“reputation matters when imperfect information is involved in which case the

dynamics of how reputations are established”… “the economic value of an artwork

depends on its credibility, which is created by the interaction of various insider

experts who are in command of cultural knowledge”…”Cultural knowledge includes

subjective elements though”. This means that according to their research, there are

no objective criteria to ascertain the quality of an artwork even though Frey and

Pommerehne‟s43 empirical results suggest that an objective aesthetic evaluation

does exist. Where talent is concerned Rosen44 argues that “talent has a multiplicative

effect on reward, which implies that small differences in talent may result in large

differences in earning”. Adler45 is somewhat more strict when stating that “large

differences in earnings could exist even where there are no differences in talent”.

Beckert and Rössel in their study in 200446 used auction and gallery data combined

with a database compiled by the Capital Kunstkompass show that the value of an

artist is a process of building a reputation through experts and institutions in the

auction as well as the dealer market. Schönfeld and Reinstaller47 in their study of the

primary market state also that prices show an “anchoring effect” of prices depending

on the growing reputation of the artists and the galleries. Price decreases are usually

41


229-247.

42

Bonus, H. Ronte, D. 1997. Credibility and Economic Value in the visual Arts. Journal of cultural

Economics vol:21 103-118. 43

Bonus and Ronte refer to Frey and Pommerehne’s book Muses and Markets – Explorations in the

Economics of the Art. Basil Blackwell, Oxford (1989). 44

Rosen, S. 1981. The economics of superstars. American Economic Review vol:71 845-858. 45

Adler, M. 1985. Stardom and talent. American Economic Review vol:75 208-212. 46

Mentioned in: Hutter, M. et al 2007. Two games in town: a comparison of dealer and auction prices

in contemporary visual arts markets. 47

Schoenfeld, S., Reinstaller, A. 2007. The effects of gallery and artist reputation on prices in the

primary market for art: a note. Journal of cultural Economics vol:31 143-153.

20

avoided as they act as reputation loss for both galleries and artists. A possible

decrease would mean that either the artist lost his audience for some reason or that

the gallery has lost faith in the artist‟s talent. Be that as it may, it still remains a

question of who is the most appropriate critic when it comes to an artwork which is

priced higher than others. When it comes to consumers, researchers agree that the

cultural consumption requires knowledge about the artist, his work and preferably

other artists of the same style. However, the extensive study of an artist and his work

will inevitably make the individual a sort of expert provided that he or she has access

to abundant information concerning the artist. When it comes to visual arts the

important step in building a reputation is to exhibit one‟s work and receive appraisal

by experts and the audience. The artist‟s reputation is also correlated with the

reputation of the gallery that represents him or her.

2.6.3.2 Age

According to Sagot Duvauroux48 the age of the artist has a positive influence

on the price as older artists have simply much more time to establish their reputation

and therefore the price level of their artworks. Agnello and Pierce‟s49 study on a large

sample of paintings by 66 American artists comes to the conclusion that the age of

the artwork has a positive, albeit non linear relation to the artwork price. The age of

the artist at the time of creation can create a bias to the buyer as it indicates a

possible period during which the artist followed a certain style and also the amount of

supply that lies before or beyond this age limit.

2.6.3.3 Gender

Gender seems to have a traditional bias for lower prices when it comes to

female artists. However the exact impact of this characteristic alone has not been

examined fully. In the case of photography which is a much younger medium in

comparison to painting which is dominated by male artists in its long history, things

seem to be different as the history of photography has several important female

creators to present and as shown in the data sample of this thesis their price levels

can be quite high.

48




investment. Journal of cultural Economics vol.20 359-383.

21

2.6.3.4 Education – Influences

The beginning for a new artist and the building of his reputation has to have

proper foundations. These foundations can either be pure talent without artistic

education, extensive education and practice besides an artist and of course both.

This is an aspect that has not been studied adequately in terms of price

determination. Apart from talent, the education and practical experience of the artists

is usually not taken into consideration. The question therefore is whether an

educated artist in a well known art school is more likely to taste success than one

that had lesser or no education. In the case of photography this is also interesting as

many artists are self taught or they have visited a certain art school. Many of them

also follow painting classes and then choose photography as their medium of

creation. However it is indeed to differentiate the term talent and the term education

as they clearly overlap with education shaping and talent and creativity greatly.

2.6.3.5 Death effect

There are also other kinds of factors that are linked to the life of the artist

(death is the most common example) and can influence the price levels of all of his or

her works. This effect has been studied by Ekelund50 based also on other studies

(Agnello amd Pierce, Czusack) which also come across to this effect in their

analyses. Their conclusions on their analysis of 21 Latin American artists who died

between 1977 and 1996 is that there appears to be a “death effect” with prices rising

substantially just after the artist passes away; however the prices fall immediately

thereafter. They attribute this phenomenon to the expectations of the demand side

concerning the apparent scarcity of the works of the artist as there is no future

supply, and compare it to the case of a durable goods monopolist as identified by

Ronald Coase in 1972. Coase‟s argument lies to the fact that if suppliers have the

ability to control scarcity of their product they can charge the consumer with a

monopolistic price.

Where art is concerned, one case is the “death effect” and another is

whenever the artist can make a contractual agreement with the buyer to ensure him

that his acquisition is unique or of limited supply. In the case of reproducible artworks

such as lithographs, woodcuts and of course photographs which is the main topic of

this study, artists tend to limit the edition of particular works by either destroying the

prototypes (plates, negatives etc) or making legal agreements like contracts or wills

50

Ekelund, R. et al. 2000. The “Death Effect” in Art prices: a demand side exploration. Journal of

cultural Economics vol:24 283-300.

22

that do not allow the reproduction of the image. One such example in the field of

photography is Richard Avedon (1923-2004) who stated explicitly in his will that no

further printing or reproduction of his negatives is allowed by anyone for any reason

except from contact printing for study purposes. This means practically that the

existing prints are the only prints that can ever enter the art market therefore the

supplied quantity is now fixed. This of course has an impact on the value of the

existing prints, especially those that are already in the secondary market. Even

though a commercial photographer himself, his will states that no commercial or non

commercial exploitation of his images is allowed.51

2.6.4 External price determinants

2.6.4.1 Expert opinion – estimation

Experts as discussed above are the catalysts in determining the value of an

artwork and consequently an artist, by contributing to the growth of reputation.52 They

are also usually the ones that potential buyers rely on to acquire information on

artists53 and therefore minimize the information and transaction costs for the

purchase of a certain artwork. This can lead to a superstar effect by narrowing down

the number of selected artists which creates an increase to their prices.54 Experts are

also the ones that determine the whereabouts of the most successful sales. For

instance Czusack55 while studying Picasso auction sales, she attributes higher prices

in New York to expert statements. Moreover she verifies this statement by showing a

58% turnover share for the United States while Great Britain and France follow with

28% and 13% respectively. It is therefore rational to assume that the estimations that

experts are calculating for each artist are a representative figure of the artist‟s

reputation with the reserve price being more to the real estimated value and the

upper end expressing the desired new level for future transactions.

51

The information concerning Avedon’s will is published online by www.allbusiness.com in an article

titled: Iron Will: Richard Avedon Leaves A $60 Million Fortune? With Strict Instructions. By Lisa

Selin Davis. Published on 2nd

June, 2006. Consulted on 7th

July 2009. 52

This is the same reference mentioned in 2.6.2.1 Reputation of the artist, quoting Beckert, J. Rössel, J.

2004. Kunst und Preise. Reputation als Mechanismus der Reduktion von Ungewissheit am

Kunstmarket. Kölner Zeitschrift für Soziologie und Sozialpsychologie vol:56 pg32-50. 53



Velthuis, O. 2003b. Visual Arts. In: Towse. R. (ed.) A handbook of cultural economics. Edward

Elgar, 470-475. 55


229-247.

http://www.allbusiness.com/

23

In general there is an effort on behalf of dealers for prices per artist to rise.

Velthuis56 notes that most dealers ignore the concept of price elasticity and are

therefore price and not profit maximizers. This is due to the perception that the rise of

price indicates success for the artist and for the institution that carries out the

transaction (gallery, auction house) and is also a confirmation of quality (Veblen

effect). The cases where the price is lowered are scarce and have mostly to do with

failure to sell in the past usually at auctions. According to Beggs and Graddy57 “if

buyers are attempting to learn about the true value of an item, which is common to all

buyers, then past failure can lead to lower prices.” Moreover, reserve prices can

“both increase and decrease the final observed price. In an art auction it is also

possible that failure may indicate that the owner has a high reserve price”…“the

seller may lower his reserve price because of an urgency to sell.”

2.6.4.2 Economic factors – Comparison with other markets

The state of the economy plays an important role in the performance of the

art market. When it comes to art as an investment then it is generally observed that

in times of economic growth the art market is influenced positively. Potential

investors may be interested to invest in art when they have experienced profits in the

stock market or the real estate market but that is not an absolute rule58. Baumol59

characterised art investment as a floating crap game by comparing the stock market

with the art market; the works of well known artists are more likely to show random

behaviour because the formation of an equilibrium in the art market is less possible

than in the manufacturing sector. Pesando60 who is dealing with auction prices for

prints, states that “the risk of investment in prints is comparable to the risk of

investing in stocks or long term bonds,” and therefore concludes that art investment

does not compare favourably with other traditional investments. Goetzmann and

Spiegel61 find “evidence of a strong relationship between the demand for art and

56

Velthuis, O. 2003a. Symbolic meanings of prices: Constructing the value of contemporary art in

Amsterdam and New York galleries. Theory and Society vol:32 pg181-215. 57

Beggs, A. Graddy, K. Failure to meet the reserve price: the impact on returns to art. Journal of


58


Basil Blackwell, Oxford, UK. 59

Baumol, W. 1986. Unnatural Value: or Art Investment as a Floating Crap Game. The American

Economic Review vol.58, pg. 933-942. 60

Pesando, J. 1993. Art as an investment: The market for modern prints. The American Economic

Review vol.83, pg. 1075. 61

Goetzmann, W. Spiegel, M. 1995. Private value components and the winners curse in an art index.

European Economic review vol.39 pg. 549-555.

24

aggregate financial wealth over the very long term, manifested by the fact that the art

index and an index of London Stock Exchange shares over the same period are

highly correlated.” Their results support Baumol‟s view of how the stock market

dominates the course of the art market. They also give an example by the auction

market when saying that “extraordinary prices obtained at auction for paintings such

as Van Gogh‟s Sunflowers occurred during an unprecedented decade for global

stock investment.” Worthington and Higgs62 conclude that there is a causal

relationship between returns in the stock and the art market, however this

relationship is not exact.

Inflation is another issue that has caused controversy; Goetzmann63 notes

that even though returns to art investment exceed inflation rates over long periods of

time, the risk is still significantly high in comparison to other forms of investment.

Agnello and Pierce‟s study64 also shows returns above inflation for most artists or

their period of 1971 to 1992. Frey and Pommerehne65 on the other hand refer to art

investment as a hedge for times of high inflation in the long run.

At this point the theoretical part is concluded. After referring to previous

studies, the next chapter will attempt to familiarize the reader with the history and

origins of the photographic medium and photographic museums. Even though a

single chapter is not enough to lay out details, some strategic points of photographic

history along with the some of the most important names give an overall satisfactory

general idea.

62



Goetzmann, W. 1993. Accounting for taste: Art and the Financial Markets Over Three centuries. The

American Economic Review vol.83 pg.1370. 64


investment. Journal of cultural Economics vol.20 359-383. 65


Basil Blackwell, Oxford, UK.

25

3. A historical summary

3.1 Introduction

In this chapter I will try to lay out a summary of some of the key points in the

history of photography in order to make the understanding of contemporary

photography and the work of the artists of the sample more feasible. The writing of a

concise history of photography is always risky as there is usually a chance that some

artists are not mentioned to the extent of their true impact on the medium. In the

constraints of this study, some major key-points in the history of photography will be

given, which explain to a certain degree the relation between painting and

photography. Some people may also call it rivalry, but in any case this chapter is

focusing on the photography side and how this medium evolved technically and

aesthetically to become part of the fine arts.

3.2 The foundation of physics and chemistry

Light, the energy that makes things visible, has been studied by civilisations

throughout history, especially those who managed to make a safe distinction

between science and religion. The ancient Greeks were the first that discovered laws

in the way light changed its course when reflected by polished surfaces and the

mathematical disciplines that characterised these laws66. Philosophy and science

were closely related at those times as different theories succeeded one another in a

quest to understand the nature of vision and light. Plato (427 – 347 BC) for instance

taught that sensitive rays came from the eyes as a form of energy, they reflected on

things and made them visible. Aristotle (384 – 322 BC) one of Plato‟s students taught

on the other hand that things themselves reflected the light rays that hit the eye thus

making them visible. As is known today Aristotle‟s opinion prevailed as it was

accepted by other scientists such as Euclid and Ptolemy. Ancient Greeks were also

aware of substances that reacted to sunlight in various ways but did not when kept in

darkness. Mythology in this case unveils its hidden elements of scientific knowledge,

preserved by the narrative power of the myth. Eder mentions a poem by Sophocles,

where the death of Hercules is described. Hercules died when he wore a garment

dipped into the poisonous blood of the centaur Nessos, which created burns while

sticking to his skin. This poisonous solution was kept in total darkness until it started

66

Eder, J.M., 1978. History of photography. New York. Columbia University Press. 4th

edition.

26

to react with sunlight. Even though they did not manage to combine these two

sciences, i.e. physics (optics) and chemistry, in order to invent photography as we

know it today, they certainly provided the fundamental knowledge for scientists of

later times to do so. To honour them the new invention was called photography,

meaning in Greek “writing with light”.

3.3 Photography did not happen accidentally

The official date that photography was announced as a patented invention is

the 19th August 1839. However photography was not invented by one person as one

would expect67. This invention was the result of favouring economic, political and

social circumstances, as well as adequate scientific development and the careful

observation and work of some creative people. The beginning of the 19th century was

starting to show the effects of the age of Enlightenment, the scientific blooming, the

beginning of nationalisation and industrialisation. The bourgeoisie class was

becoming more and more powerful, demanding to have some of the things that one

or two centuries ago would be a luxury only for the eyes of aristocrats. This could

only be possible with machines, which could help people overcome some of the

basic constraints of their lives such as transportation (steam engine) and so on.

Looking back at the 19th century one could say that the birth of photography was the

invention of another machine that was able to capture and reproduce realistic images

of nature. Until that time painters and designers used the camera obscura (dark

room) as a means to draw more realistically. The camera obscura is a totally dark

room that concentrates, by means of a hole or a lens, the rays of light onto the

surface on the opposite side, thus creating an upside-down image of the scene. This

principle was first described by Aristotle and thanks to Arab alchemists of the 11th

century it reached the Renaissance and Leonardo Da Vinci who was the first to fully

describe the camera obscura. From that point on it was used occasionally to assist

painters. By the time of photography it was used extensively to produce realistic

sceneries, portraits (silhouettes) etc. There was a way to form a realistic image but

there was still no other way to capture it on paper and sustain it but to draw by hand

and the process of making additional copies was as difficult as making the original.

Chemistry on the other hand had also evolved to allow experimentations with light

sensitive materials, how to make them light sensitive, how to make the image visible

and finally to be able to maintain that image (fix it). The people that actually managed

to combine these factors are the inventors of photography. The title “father of

67

Frizot et al. 1994. History of photography. Koeln, Koenemann Vmbh. Edited by Michel Frizot.

27

photography is divided today among several people however the most attributed are

Joseph Nicéphore Niépce68, Louis Jacques Mandé Daguerre69 and William Henry

Fox Talbot70 71. It is also notable how these people developed their processes at

approximately the same time period from the 1820s until the late 1830s. Of these

processes some were developed furthermore and others were abandoned due to

their weaknesses. The abandoned processes continued to be used or were

rediscovered at later times by passionate amateurs and are commonly regarded

today as alternative photographic processes, used almost exclusively for artistic

purposes.

During the last two decades a revolutionary change has altered the world of

photography in the form of digitalisation. Film is no longer the most efficient means of

capturing an image giving its place to electronic sensors. Chemistry therefore has

given most of its place in the photographic medium to physics and its specialisation,

electronics. Commercial photography was the first to embrace the new medium due

to its great improvements in controlling results and efficiency, but photographers also

use digital technology for artistic reasons as well. This fact, as every change in

technology and methods, has created a quarrel concerning aesthetics, quality and

artistic value, like stone creating ripples on a still pond. It is empirically known that the

pond will become still again at some point in the future.

3.4 Photography as fine art and the different genres

The great exhibition at the Crystal Palace in London in 1851 was probably the

first time the public became aware of the possibilities of the new medium. The initial

impact of the photographic invention was striking for the art world. Eugene Delacroix

stated that “Painting is now dead”. There was now a mechanical way of creating

realistic images that a painter or graphic designer would possibly reach with much

more time and skill. Photography certainly forced commercial portraitists and scenery

painters to become photographers or change their profession. The commercial

character of photography was and is the main reason why it has strived to be

recognised as an art. Photographers since photography was invented have tried to

be regarded as artists. Especially after the 1850s where photographers had a

process that they could rely on more or less, they also had the impression they would

68

Joseph Nicéphore Niépce (1765-1833) 69

Louis Jacques Mandé Daguerre (1787-1851) 70

William Henry Fox Talbot (1800-1877) 71

Marien, M.W., 1997. Photography and its critics. Cambridge, UK. Cambridge University Press.

28

become part of art history. Photography‟s effort to imitate compositions of paintings

shows part of this effort. Oscar Rejlander72 made such a photographic composition

called “The two ways of life” in 1857, a print that consisted of nearly thirty different

negatives, a task that required six weeks to complete and of course great skill for

those times73.

Oskar Rejlander, The two ways of life, 1857

Depicting a father and his two sons looking at different ways of life, it symbolises the

boundaries between rural (pure) and urban (sinful) lifestyles. This photograph

created controversies at its time and was characterised as indecent and

inappropriate.

Other photographers of the time like Henry Peach Robinson74 combined

photography with watercolour painting in a perhaps attempt to make an artistic bridge

over the two visual art media. He is also known for his pictorialistic compositions like

Rejlander thus giving his images an expression of feeling rather than a depiction of

facts.

The most known woman photographer of the time, Julia Margaret Cameron75

following the same principle chose to take slightly out of focus and motion blurred

images to achieve the plasticity of paintings while choosing subjects derived from

themes of paintings.

The technological evolution of the late 18th century not only in photography

but also in other fields which are closely related, affected the way photography was

72

Oscar Rejlander (1813-1875) 73

Frizot et al. 1994. History of photography. Koeln, Koenemann Vmbh. Edited by Michel Frizot. 74

Henry Peach Robinson (1830-1901) 75

Julia Margaret Cameron (1815-1879)

29

perceived by the public, and also increased the number of photographs taken and of

course the number of photographers.

Henry Peach Robinson, Fading away, 1858

Experimentation on photomechanical processes led to the invention of the

halftone process which allowed photographs to be directly reproduced in publications

such as periodicals and newspapers. As a result the visual information provided to

the general public increased to an unprecedented degree. Long columns of

descriptive texts were now substituted by photographs using the halftone process

instead of the time-consuming engravings and line drawings that appeared

occasionally on a page.

This was the kick start for photojournalism to flourish but also for commercial

photography to take the place of sketches and other means of manual depictions. In

the photography section itself the introduction of Kodak camera by George

Eastman76 (1888) brought yet another revolution in the medium. Thousands of

amateurs were now able to take pictures and to have them developed thus avoiding

hours of processing in household rooms turned into darkrooms. The company slogan

“You press the button, we do the rest” enticed people to carry a relatively small

camera and take lots of spontaneous photographs without being concerned with

technical details. The introduction of film instead of glass plates also allowed the size

and weight of cameras to drop significantly and make them suitable even for children.

These developments had of course an impact also on professional photography as

76

George Eastman (1854-1932)

30

portraitists were no longer essential for taking a simple snapshot. Thus more

amateurs that had no previous experience in art became active in photography as

they had the chance to get involved with the medium in a less costly and time

consuming manner.

The mass production of photographs by more and more people in the

beginning of the 20th century could not be overlooked by art photographers. In 1902

Alfred Stieglitz77 launched the Photo Secession in an effort to discriminate pictorialist

and abstract photographers from all other kinds of photography with exhibitions in

New York and London78.

Alfred Stieglitz, The Steerage, 1907

He also published a magazine called Camera Work where he promoted the

new art photography as he called it, but also enlarged the scope of the periodical to

include other arts and art theory. Photo Secession had also its own “home” in

Stieglitz‟s former studio at 291 Fifth Avenue, which became internationally known as

“Gallery 291”. Artworks of painters were also shown at that gallery including Auguste

Rodin, Henri Matisse, Pablo Picasso, Paul Cezanne and others. This was a strategic

move of Stieglitz to shift photography to the levels of fine art next to the traditional

77

Alfred Stieglitz (1864-1946) 78


31

arts. Much criticism evolved from this, mainly accusing the pictorialist photographers

that they were “mixing deliberately technical devices of painting with photography

thus creating an injustice”79. The Photo Secessionists on the other hand advocated

that their self expression was soul preserving in a world of mass production and

mass taste. Apart from Stieglitz, also well known photo secessionists are Edward

Steichen80, Clarence White81, Gertrude Käsebier82, Frank Eugene83, Alvin Coburn84

and others. The Camera Work was published until June 1917, where at the last issue

photographs of Paul Strand85 were shown.

Edward Steichen, Flatiron, 1905

Paul Strand did not come into photography through art but through social

concern, being a student of Lewis Hine at the New York Ethical Culture School. Hine

is well known for his pictures of under-aged factory workers, pictures that forced the

formulation of legislation to control industrial hiring practices. Paul Strand‟s pictures

made street photography popular using photography in a “brutally straightforward

way” in Stieglitz‟s word.

79

Marien, M.W., 1997. Photography and its critics. Cambridge, UK. Cambridge University Press. 80

Edward Steichen (1879-1973) 81

Clarence White (1871-1925) 82

Gertrude Käsebier (1852-1934) 83

Frank Eugene (1865-1936) 84

Alvin Langdon Coburn (1882-1966) 85

Paul Strand (1890-1976)

32

Paul Strand, Blind, 1916

After WWI the mass press media were growing at a rapid pace. More and

more newspapers and magazines were published that made photojournalism more

popular. Social concern, even though the oversupply of pictures of poor people

created a “compassion fatigue”, did not lose ground. But apart from photojournalism

art photography was not left behind in the Photo Secession‟s movement of

pictorialism and abstract photography. Artistic genres such as surrealism, dada and

futurism were also expressed through the photographic medium especially in Europe.

The Russian revolution had a deep impact on artists as the new regime encouraged

revolutionary artists experimenting with cubism and futurism. In that period

photomontage was revisited after over 50 to 60 years since Rejlander. However

these photographic experiments originated by German and Soviet experimental

artists had completely different social roots than their Victorian-era predecessors.

Famous photographers of this era are Alexander Rodschenko86, Andre Kertesz87,

Lazlo Moholy Nagy88, Man Ray89 and others90.

However these artistic oeuvres did not make a great impression when they

crossed the Atlantic.

86

Alexander Rodschenko (1891-1956) 87

André Kertész (1894-1985) 88

Lazlo Moholy Nagy (1805-1946) 89

Man Ray (1890-1976) 90


33

Alexander Rodtschenko, Stairs, 1930

In the mists of Pictorialism in the late 1920s a group of photographers issued

a manifesto which was called “Group f64” advocating that photography should

remain independent of ideological conventions of art (such as pictorialism) and it

should develop within the actualities and limitations of the photographic medium. The

group‟s name derived from the use of the smallest aperture level possible (f64) that

provided the greatest amount of depth of field, thus allowing both the foreground and

background of the image to remain clear (in focus).

Ansel Adams, Tetons River, 1942

34

Ansel Adams91 and Edward Weston92 are probably the best known members

of the group. Apart from f64 the social circumstances in the United States brought a

new wave of social concern pictures. The Depression era following the economic

crisis of 1929 was depicted with striking images in the 30s by Dorothea Lange93 and

Walker Evans94. In Europe the 30s are also suitable for social photojournalism with

Brassai95, August Sander96 and of course Henri Cartier Bresson97, who set the

foundation of further interpretation of a street photograph with the introduction of the

“decisive moment” of taking the picture. August Sander comes as a great influence to

many later artists like Becher and Gursky with his direct approach and photographic

objectivity using no awkward angles or experimenting with photographic materials.

His major project was the compilation of portraits from different occupational types

starting from farmers and industrial workers to professions, artists and ending with

the unemployed and disabled. Sander‟s work is considered an international reference

point of conceptual artists.

August Sander, Hod carrier, 1929

91

Ansel Adams (1902-1984) 92

Edward Weston (1886-1958) 93

Dorothea Lange (1895-1965) 94

Walker Evans (1903-1975) 95

Brassai (Gyula Halasz) (1899-1984) 96

August Sander (1876-1964) 97

Henri Cartier Bresson (1908-2004)

35

During the 40s however, a tendency to shift back to abstraction was once

more evident especially in the United States. In general abstract photography,

although not a major trend in photography as a whole, returned repeatedly after the

Second World War challenging the general idea that photography should record the

reality. Artists of this period that refered back to Stieglitz and Rejlander as their

references are Aaron Siskind98, Minor White99, Lotte Jacobi100 and Jerry

Uelsmann101.

Minor White, like Stieglitz, insisted that photography could be something more

than the literal depiction of optical reality and preferred to work in series of abstract

pictures arranged so as to depict poetic meanings. If he had lived in the end of the

19th century he would probably be a pictorialist, but at this period one could say he is

the aesthetic combination of pictorialism (Stieglitz), pure photography (f64) and

abstract photography (His teachings across the United States allowed him to inspire

young photographers to have a personal and spiritual insight in their work. In 1952 he

became editor of art photography magazine Aperture which he founded together with

Ansel Adams, Dorothea Lange and others.

Minor White, Metal Ornament, 1957

Jerry Uelsmann was also a student of Minor White and his superb

combination printing technique allowed him to create alternative worlds where nature

98

Aaron Siskind (1903-1991) 99

Minor White (1908-1976) 100

Lotte Jacobi (1896-1990) 101

Jerry Uelsmann (1934)

36

operates in a different manner than in reality. Surrealism in photography had lost its

feeling of anger over political circumstances and was now more of a psychological

inquiry and imagination. Uelsmann made also direct references to the 19th century

pioneers of photography. One of his most well known pictures showing a floating tree

over a small piece of land and a lake is an image made in 1969. Today our visual

experience is much more saturated by images like this, as the extensive use of

sophisticated software allows us to create such images and even more complex in a

much easier way and with practically endless possibilities. However at that time this

was a product of great skills, notable labour time and of course artistic creativity. The

fact that these factors are taken into consideration by a large portion of viewers and

critics today makes it difficult to compare the value of images of more or less similar

aesthetics (abstract for instance) especially when they are made at different times

and with different technological methods. The conversation over value is literally

endless but the most important aspect perhaps is to comprehend its multidimensional

nature.

Jerry Uelsmann, Untitled, 1969

Street photography acquired a new dimension in the post war period. Art

photographers also started to practice street photography trying to express their inner

feelings instead of just depicting the street‟s reality. In this period famous and

influential artists appeared such as Lee Friedlander102, Garry Winogrand103,

102

Lee Friedlander (1934) 103

Garry Winogrand (1928-1984)

37

Weegee104 (Arthur Fellig) and Diane Arbus105. Arbus‟s pictures of children, many of

them from the streets of New York, are distinctive as she reveals them as “little

versions of mean-spirited adults”106. At first glance most of her pictures seem like

everyday children snapshots but a closer look starts to give out all small details that

result to a completely different perception of the picture. Arbus is yet another

conceptual artist, who has influenced greatly the generations of artists afterwards,

among them Rineke Dijkstra included in the sample of the analysis.

In the 60s and 70s the use of colour photography was becoming more and

more often. Again commercial photography would be the first to embrace the new

trend but art photography was soon to follow.

Form and lighting were not enough to express artistic needs and “colour

challenged the photographer‟s ability to resist its simple prettiness and devious

nullification of form” according to John Szarkowski, director of the Photography

department at New York‟s Museum of Modern Art from 1962 to 1991, successor of

Edward Steichen.

Diane Arbus, Child with toy hand grenade, 1962

His contribution to the reinstating fine art photography in New York and thus

launching the career of many art photographers is beyond any doubt. This phrase on

colour comes from a book he wrote in 1973 “How to look on photographs” and uses

as an example the work of William Eggleston107 who is one of Andreas Gursky‟s

major influences concerning the use of colours.

104

Weegee (Arthur Fellig) (1899-1968) 105

Diane Arbus (1923-1971) 106

Marien, M.W., 1997. Photography and its critics. Cambridge, UK. Cambridge University Press. 107

William Eggleston (1939)

38

William Eggleston, Greenwood Mississippi, 1970

At this point this brief representation of photographic history will be over as it

starts to overlap with work which is considered contemporary and therefore falls to

the genre of artists which are chosen for the analysis of this study.

Even though the history of photography is less than 200 years old, it cannot

be presented adequately in a small chapter. The purpose of this chapter was to give

a slight idea of the photographic timeline with some important points of reference and

with the assist of visual examples. It is probably unfair to judge an artist aesthetically

by a single image, but in texts concerning photography it is always helpful to have an

image present. Important photographers have been left out, or mentioned but not

analysed enough due to certain constraints. The major constraint is the sample, the

description of which will follow in the methodology chapter. The 12 artists chosen are

a fragment of the population of artists active in the last 30 years both recognised and

mentioned by art photography books but also others who work in obscurity. The

purpose of this study is not to judge whether these 12 artists are a good fit sample to

describe the population of art photographers aesthetically, but they are certainly

recognised after decades of work for some reason. The expert opinion is the one that

will define in the end of the day, who is the one to be recognised or not and in a world

of millions of images, thousands of photographers and artists that is a difficult task.

3.5 Photography museums

The term “photography museum” was used from a very early stage in

photographic history when some of the first studio owners and also pioneering

independent photographers used it to describe their establishments: for instance, the

39

Macaire brothers108 in Paris at the end of the 1840s when they proposed the creation

of a department that would collect everything that the photographic medium would

have to offer. Hermann Krone109 in Dresden a few years later for the collection of

plates he assembled to demonstrate his techniques. He created the “Historical

Didactic Museum of Photography” an exhibition of techniques with illustrations with

the purpose to make people more accustomed to the medium110. At this time very

few public institutions were interested in photography. The earliest collections owed

their creation to private initiatives with the formation of photographic associations like

for example the London Photographic Society, later the Royal Photographic Society

(RPS), and the Société Française de Photographie). An exception showing

government interest on the medium, was the South Kensington (later Victoria &

Albert) Museum, whose director Henry Cole from 1852 bought and commissioned

photographs for both teaching and documentary purposes. It was also the

establishment that hosted in 1854, the first photographic exhibition to be held in a

museum with royal patronage111. However this initiative was perhaps the only one

occurring at that time. Photography was to be exhibited for documentation or

scientific purposes because of its technological advantages. It was not until the

1930s, as described earlier during the short historical summary, that photography

modestly began to gain access to galleries and also a museum as an autonomous

artistic medium. Alfred Stieglitz gave several hundred photographs to the Department

of Prints of the New York Metropolitan Museum in 1928 and 1933. The year 1927

saw the opening of the Kodak Museum at Harrow, near London. The same year, the

Musée des Arts et Métiers in Paris dedicated one of its galleries to a strictly technical

survey of photography and cinematography. In 1935, the San Francisco Museum of

Art acquired its first camera images. However, it was the Museum of Modern Art

(MoMA) New York, which created the first photography department in an art

museum, taking place in 1940 with the initiative of Alfred Barr and Beaumont

Newhall. The next important step in the establishment of photography in museums

was the transformation of the house of the late George Eastman, founder of Kodak,

into a museum of the history of photography in 1949. Its collection was the first of this

scale internationally and still one of the most important. In Essen, Germany, Otto

Steinert created a photography department at the Folkwang Museum in 1959.

108

Louis-Cyrus Macaire and Jean-Victor Macaire-Warnod, known as "les frères Macaire" (French,

1807–1871; French, 1812–after 1886). 109

Hermann Krone (September 14, 1827 – September 17, 1916) 110

Boom, M. Rooseboom, H. 1996. A New Art: Photography in the 19th Century. The Photo

Collection of the Rijksmuseum, Amsterdam. 111

Haworth-Booth, M. 2004. Photography: An Independent Art. Photographs from the Victoria &

Albert Museum 1839-1996. V&A Publications, London.

40

Thanks to the initiative of an amateur photographer and collector, André Fage,

France's first photographic museum was founded at Bièvres. The general interest in

the history of photography and its inventors prompted other local initiatives: in 1972,

for example, the Musée Niépce opened at Chalon-sur-Saône, organized initially

around the Niépce archives. Three years later, Henry Fox Talbot's home at Lacock

Abbey opened to the public.

Today, institutions dedicated to photography are both numerous and very

varied in structure, approach, and size. They can be divided into four categories. One

category is that of the museum specifically dedicated to the history of photography

(and sometimes also cinematography) which cover technical, artistic, sociological,

and cultural aspects from the invention and before to the present. Primary example is

the George Eastman House mentioned above but also the National Museum for

Photography, Film, and Television in Bradford (founded in 1983), which today houses

numerous British collections of images and equipment. In France there is the Musée

Niépce dealing with one of the most important collaborators in the birth of the

photographic medium. In Belgium museums of photography can be found in Antwerp

(founded in 1965) and Charleroi (1987). Notable are also the Museum of Hungarian

Photography in Kecskemét (1990), the Netherlands Fotomuseum in Rotterdam112

(2003) and the Museum of Photography in Thessaloniki113 (1987).

A second category is more specifically focused on photographic technology,

dealing often with collections of photographic cameras like the Swiss Camera

Museum at Vevey (1971), the camera collection of the National Museum of

Technology, Prague (reorganized 1983), and the Canon Camera Museum in Tokyo.

A third category is the one of combined visual arts such as a museum of

paintings and sculptures incorporating photography. Most large North American

museums (the Metropolitan Museum, Art Institute of Chicago, Boston Museum of

Fine Arts, National Gallery in Washington, DC, J. Paul Getty Museum, San Francisco

Museum of Art, Houston Museum of Fine Arts, Amon Carter Museum, Fort Worth,

etc.) had either created photographic departments by the end of the 20th century or

at least become seriously active in the field. This idea also caught on in Europe: for

example, in Britain (the Victoria & Albert Museum, National Portrait Gallery, and

National Galleries of Scotland), Paris (the Musée d'Orsay, Musée National d'Art

112

http://www.nederlandsfotomuseum.nl/ (Consulted on 7th

July 2009) 113

http://www.thmphoto.gr/ (Consulted on 7th

July 2009)

http://www.nederlandsfotomuseum.nl/

http://www.thmphoto.gr/

41

Moderne-Centre Pompidou), Vienna (the Albertina) and the Netherlands

(Rijksmuseum). Outstanding curators such as John Szarkowski (MoMA), Weston

Naef (Getty), and Mark Haworth-Booth (Victoria & Albert) have been important in

integrating photography into the wider museum scene. Many museums of

photography are now affiliated to museums of fine or contemporary art: e.g. the Agfa

Photo-Historama in Cologne linked to the Ludwig Museum, the Hague Museum of

Photography built attached to the Gemeentemuseum Den Haag, the Fotografiske

Museet in Stockholm, close to the Moderna Museet; and the Canadian Museum of

Contemporary Photography affiliated to the Ottawa Gallery of Fine Art114.

A fourth less common category is that of the museum or foundation created

around a particular photographer or establishment. Notable, apart from Lacock

Abbey (Henry Fox Talbot), are the Alinari Foundation in Florence; the Primoli

Foundation in Rome; and the Cartier-Bresson Foundation in Paris (2003). Finally,

many non-photographic museums and other institutions hold extremely important

photographic collections: in London alone, for example, the British Library, Imperial

War Museum, National Maritime Museum, Royal Geographical Society, and

numerous others.

The development of the Internet has brought with it the creation of „virtual‟

photographic museums - e.g. the American Museum of Photography115 - almost as

varied in size and content as their bricks-and-mortar counterparts.

These institutions have nowadays a twofold role depending on their

specialisation described above. First they provide historical information for the course

of the photographic medium in order to show the difficulties of earlier techniques but

also the accomplishments of the photographers of the past. Second museums are

prestigious exhibition centres for contemporary artists, which could influence a dealer

or an expert in the price estimation phase of the artworks. Museums have also a

greater opportunity to bring new artworks to more people as they are in general more

well known by the general public and their accessibility is usually greater than

galleries hosting an individual exhibition from time to time.

With the conclusion of the historical summary in the present chapter, the

following will present the sample of contemporary artists chosen for the analysis.

114

Haworth-Booth, M. 2004. Photography: An Independent Art. Photographs from the Victoria &

Albert Museum 1839-1996. V&A Publications, London. 115

http://www.photographymuseum.com/ (Consulted on 7th

July 2009)

http://www.photographymuseum.com/

42

4. Data This chapter presents the sample of 12 artists with a short biography for each

one and a series of descriptive statistics on the database.

4.1 Sample overview

The data is comprised from the auction sale records of Artprice.com. The

search for the artists themselves was not particularly difficult, as some maintain their

personal websites but it is logical to assume that no one is willing to publicize

information concerning the prices of their artworks. Only extreme cases such as 7-

figure hammer prices can be found online for free. The auction houses themselves

are not reluctant to disclose their records of transactions to the public, but their

websites are more customer oriented, which is absolutely rational, and not data

archive oriented as Artprice.com.

Artprice.com has been an apocalypse in this sense. Artwork transaction

database dates as far back as the early 90s and provide adequate information

concerning the size of the artwork, the year it was created, the year it was sold by the

auction house and at what price, including an automatic change in euro currency.

Even if the prices are not in terms of today‟s prices they are certainly the best

possible indicator there is.

Regarding the abundance of information gathered online, some minor

inconsistencies can be overlooked or dealt with in a respective manner. One of such

is that there is no universal use of terms that makes it harder to include a variable of

“material” in the analysis if you have no picture of the artwork itself. Each auction

house has probably its own description for each photographic artwork but even there

one can find several ways of defining the same thing with the possibility that crucial

information is omitted. For instance one encounters while skimming through the data

the use of the term “photograph” for describing the type of print, without specifying

whether it is colour or black and white. Even though there is usually a preview picture

alongside every transaction, in some cases it can also be misleading. If a comparison

can be made, it is almost as using the term painting without defining oil or

watercolour on canvas. However the frequency of those is low enough to allow the

use of the type of print as a possible determinant of the artwork price.

43

4.2 Artists

The choice of artists for the analysis was a big question because of the great

number of photographers throughout the history of photography and today. First of all

I decided to include only living contemporary artists. The comparison between living

and deceased artists would only add a possible bias to the analysis. Deceased artists

are no longer able to produce artworks and therefore the number of their artworks

becomes constant. (In rare cases unknown artworks of an artist can be discovered at

later stages). Scarcity acts therefore as an important price factor that makes the

comparison with contemporary artworks more difficult or even vain. One example is

Richard Avedon whose control over the scarcity of his works was mentioned in

Chapter 2. This of course will have a definite affect on their price which is interesting

to investigate provided the appropriate data can be acquired.

The goal was also to comprise an as far as possible representative sample of

the photography auction market not by choosing the most expensive artists but from

a spectrum of low to high. Of course with the lack of a total photography percentage

of the auction market it is difficult to identify the analogy of the sample with the

population in terms of number of cases and turnover. Due to the limitations of a

master thesis the gathering of more data was not feasible. The choice was made

much easier with the help of a book by Michael Fried116 titled “Why photography

matters as art as never before” where he states some names and explains why he

considers their work important as well as shares his personal experience with some

of them. Most of the names I chose to deal with are from this book and to those I

added some names from my personal experience. The artists are Andreas Gursky,

Bernd & Hilla Becher, Candida Höfer, Cindy Sherman, Hiroshi Sugimoto, James

Welling, Jeff Wall, Philip Lorca DiCorcia, Rineke Dijkstra, Thomas Demand, Thomas

Ruff and Thomas Struth. From those only Bernd Becher has passed away in 2007

but since he had worked together with his wife Hilla, I have decided to include them

in my list. In the text following I would like to present these artists in a brief manner.

Further details in descriptive statistics will follow after the presentation of all 12

artists. The sequence of the artists‟ presentation is absolutely random and they are

only arranged alphabetically by their first name. Some biographical information has

been retrieved from the online database of the Rijksbureau voor Kunsthistorische

Documentatie (RKD)117.

116

Fried, M. 2008. Why photography matters as art as never before. New Haven, USA. Yale University

Press. 117

www.rkd.nl (Consulted on 25th

June 2009)

http://www.rkd.nl/

44

4.2.1 Andreas Gursky

Gursky (born 1955 in Leipzig) is a German photographer who lives and works

in Duesseldorf and is mostly known for his large size architecture and landscape

colour photography. He studied photography in Duesseldorf at the Kunstakademie

and received strong influence by his teachers Bernd and Hilla Becher. His pictures

are usually from a higher vantage point and are depicting large anonymous urban

spaces in great detail, such as facades at night, office lobbies, stock exchanges, the

interiors of retailers etc especially since the mid 1990s. He has taken part in

numerous group exhibitions around the globe as well as solo exhibitions mostly in

Europe and the United States. Even though his most expensive works have been

sold in London the majority of works sold is in New York by the three major auction

houses i.e. Christie‟s, Sotheby‟s and Phillips De Pury and Company. He currently

holds the record of the artist whose picture has fetched the highest price for a

photograph. 99 Cent II Diptychon (2001) was sold in 2007 for the dazzling price of

$3,346,456 almost €2.5 million by Sotheby‟s in London. It is also interesting to

observe that the second, third and fourth places in the list of most expensive

photographs (as by 2007) are held by masters of photography (Alfred Stieglitz and

Edward Steichen) whose

pictures (taken in 1904 and

1919 respectively) were also

sold by Sotheby‟s in 2006

long after the death of the

artists. However as another

picture by Gursky was sold

for €1.7 million in 2008 the

list will probably soon consist

by his name at the top

positions.

Andreas Gursky

99 Cent II Diptychon (2001)

206cm x 340cm

Sold for €2,277,000 in 2007.

45

4.2.2 Bernd & Hilla Becher

Bernd (1931 – 2007) and Hilla (1934) Becher were an artist couple mostly

known for their extensive coverage of industrial buildings. They both studied at the

Kunstakademie in the late 50s until the early 60s and also taught there for many

years. Among their students were Andreas Gursky, Thomas Ruff, Candida Höfer and

Thomas Struth who are also in the list of photographers chosen for this research.

They searched for the forms and designs of disappearing industrial structures such

as barns, water towers, storage silos, and warehouses taking black and white

pictures from different angles but always with a straightforward and objective point of

view. For this purpose they have travelled in both Europe and the United States

throughout their career to discover such building sites. Their highest price at the

auctions has reached by early 2009 approximately €160,000. New York is also the

place where most works have been sold with London being in the second place and

Cologne being close behind in third place.

Bernd and Hilla Becher’s 1972 study of concrete cooling towers. A similar composition

was sold for 158,763€ in 2001 by Christie’s new York.

46

4.2.3 Candida Höfer

Candida Höfer (1944) is a German photographer born in Cologne and also a

student of Bernd Becher at the Kunstakademie. Her work consists of empty interiors

or social spaces in a straightforward way as her teachers and influences the

Bechers. She works in colour and has gradually throughout her career tried to

increase the size of her final prints by shifting from small format to medium format

and lately to view cameras (large format). The highest auction price for her work so

far is €78,400 from Phillips de Pury and Company in London in 2007. Once again

New York is the place where the majority of works are being sold.

Candida Höfer, Palazzo Pisani Moretta, Venezia I, 120 x 143.5 cm. 2003 sold for €78,400

in 2007.

4.2.4 Cindy Sherman

Cindy Sherman (1954) is an American photographer mostly known for her

conceptual self-portraits. She began studying painting at the Buffalo State College

but soon realised that photography was more her medium to express her ideas. Her

most well known series are the Untitled Film Stills where she takes pictures of herself

in various costumes resembling actresses from B movies and film noir. She currently

lives and works in New York City. She is considered as a prime example of an artist

using photography as her medium rather than the opposite. Her most expensive work

47

was also sold there in 2007; Untitled No92 fetched €1,364,930 by Christie‟s. As

expected from an American artist the vast majority of works (more than 80%) are

sold in New York.

Cindy Sherman, Untitled No.92, 58.9cm x 120.1 cm 1981 sold for €1,364,930 in 2007.

4.2.5 Hiroshi Sugimoto

Hiroshi Sugimoto (1948) was born in Tokyo where he also studied politics and

sociology before moving the United States where he retrained as an artist and

received his Bachelor in Fine Arts at the Art Center College of Art and Design in Los

Angeles in 1972. In 1974 he moved to New York. His work is exclusively in black and

white (gelatine silver prints) consisting mostly of architecture photography,

landscapes and portraits. He is perhaps the most well known Japanese photographer

His most well known series of work consists of old American movie palaces and

drive-ins, exposing the film for the duration of the entire film, with the film projector

providing the sole lighting. He has been nominated with the Hasselblad Foundation

International Award in 2001. In 2007 a compilation of three landscapes was sold for

€1,217,370 by Christie‟s New York. This year the famous group U2 chose

Sugimoto‟s seascape titled “Boden Sea” for the cover of their new album “No line on

the Horizon” released on March 2009. The same picture had been used by sound

artists Richard Chartier and Taylor Deupree for their LP Specification.Fifteen in

2006 who were inspired by the Seascape series of Sugimoto.

48

Hiroshi Sugimoto, Aegean Sea, Pilion. 153cm x 182.5cm 1990. Sold for €601,065 in 2008.

4.2.6 James Welling

James Welling (1951) is an American artist self taught in photography. Based

in Los Angeles, Welling has become known for his works of abstract photographs

using materials like aluminium foil and velvet.

James Welling, 023, 121cm x 94.6cm 2007. Sold

for €12,940 in 2008.

In the past 30 years he has extended his portfolio from black and white railway

photos to colour abstract photograms (use of objects directly on light sensitive

49

material without exposing a negative). Though his works have not reached the

enormous 6 to 7-figure amounts of other artists he is nevertheless a very important

contemporary artist appreciated worldwide.

4.2.7 Jeff Wall

Jeff Wall (1946) is a Canadian photographer born in Vancouver. He studied

art at the university of British Columbia where he has also been teaching for many

years. He is mostly known for his large scale back-lit cibachrome transparencies

(transparencies in lightboxes) and his art historical writings and essays on

contemporary artists. His most expensive work, a transparency in a lightbox, reached

a hammer price of €682,290 by Sotheby‟s in London. His reputation as a lecturer and

an artist is worldwide. He was in fact offered the chair of photography in Duesseldorf

Kunstakademie in 1996 as a successor of Bernd Becher who was retiring, a position

he had to reject after an unusual incident occurring at the very first day to meet his

new class.

Jeff Wall, The well, 229cm x 189cm, 1989. Sold for €682,290 in 2008.

A former Becher student threatened him with a loaded gun and Wall was forced to

resign. Becher himself was very annoyed by the passivity that the academy showed

50

for the incident and the chair was eventually given to a student of Becher‟s, Thomas

Ruff.

4.2.8 Philip-Lorca diCorcia

Philip-Lorca diCorcia (1951) was born in Hartford, Connecticut. He attended

the School of the Museum of Fine Arts, Boston, where he earned a Diploma in 1975

and a 5th year certificate in 1976. He received a Master degree of Fine Arts from

Yale University in Photography in 1979. His work alternates between daily urban life

snapshots to staged compositions.

Philip Lorca diCorcia, Mary and babe, 51cm x 61cm, 1982. Sold for €55,694 in 2000.

4.2.9 Rineke Dijkstra

Rineke Dijkstra (1959) is a Dutch photographer and video artist mostly known

for her portraits. She attended the Rietveld Academie in Amsterdam from 1981 until

1986. She usually takes portraits of people in plain backgrounds indoors and

outdoors and usually works in series. Her most distinctive work is “the Beaches”, a

series started in 1992 and was complete by 1996, which generally feature body

51

portraits of children or adolescents against a seascape. She became internationally

appreciated when she was invited to Venice Biennale in 1997. Her works can be

seen at the Stedelijk Museum Amsterdam, the Folkwand museum in Essen and the

Museum of Modern Art in New York. Her highest hammer price at an auction so far is

a compilation of portraits from the “Beaches” sold in 2002 for almost 400,000€ by

Christie‟s New York.

Rineke Dijkstra, Hilton Head Island, 1992, 190cm x 156 cm. Sold for €395,064 in 2002.

4.2.10 Thomas Demand

Thomas Demand (1964) is yet another example of contemporary German

photographic artist who lives and works in Berlin. His work consists of compositions

of three dimensional objects that look like real images of rooms and other spaces

with often no sign of human presence. His studies included art studies at the

Akademie der Bildenden Künste in Munich from 1987 to 1989, at the Kunstakedemie

Düsseldorf (1989-1992), at Cité des Arts in Paris in 1992, and at Goldsmiths College

in London from 1993 to 1994. He entered the auction market in 2000 and his highest

hammer price so far is 174,130€ in 2006 by Christie‟s New York.

52

Thomas Demand, Raum, 1994, 183cm x 270 cm. Sold for €160,000 in 2008.

4.2.11 Thomas Ruff

Thomas Ruff (1958) is a German photographer, who also studied at the

Kunstakademie Düsseldorf from 1977 until 1985. His main topics include portraits,

interirors of german living quarters, nudes which are digitally manipulated, night skies

and others. During his studies in Düsseldorf he developed his conceptual serial

photography with room portraits of friends and acquaintances and design details of

interiors from the 1950s to 1970s in Germany. He also taught the photography class

at the academy from 2000 to 2005, taking the place of his former teacher Bernd

Becher. He has had numerous exhibitions across Germany, in several European

countries and worldwide. His debut in the auction market was in 1991 and his highest

hammer price so far is 111,929€ in 2001 by Christie‟s London.

53

Thomas Ruff, Stern 02h 56, 1989, 252cm x 180 cm. Sold for €111,929 in 2001.

4.2.12 Thomas Struth

Thomas Struth (1954) is a German photographer mostly known for his

interiors of buildings, portraits and photographs of empty streets with the lack of any

human presence. He studied painting in the Kunstakademie Düsseldorf in the class

of Gerhard Richter and photography with Bernd Becher; Struth is another example of

Becher‟s influence such as Höfer and Gursky. His most recognised work is a series

of museum interiors he started in 1989 and finished in 2002. He has had exhibitions

in many countries around the globe i.e. Munich, Cologne, Brussels, Amsterdam,

Washington and New York which gave his wok international acknowledgement. He

currently lives and works in Düsseldorf, Germany. His top hammer price so far was in

2007 in New York where his cibachrome print of Pantheon in Rome was sold for

616,230€ by Christie‟s.

54

Thomas Struth, The Pantheon, Rome, 1990, 184.2cm x 238.2 cm. Sold for €616,230 in 2007.

At this point I have concluded the presentation of the photographers of my

sample. The pictures illustrating the presentation are showing some of the artists‟ top

hammer prices (in some cases the highest) and do not represent the average

hammer price. In the following section of the Data chapter I will provide some

descriptive statistics for the sample presented above.

55

4.3 Data description

4.3.1 Comparison between fine art categories

Before I begin the description of the sample data, I would like to show a very

interesting graph (Graph 1) derived from a quarterly data table provided by

Artprice.com. that shows the levels of prices of fine art artworks sold at auction

divided per category. This graph alone could be enough to explain why photography

deserves to be studied on its own. Apart from the “new market” argument the

potential of its price level is prominent. Photography has managed to double its price

levels in a period of 17 years (1990 – 2007), while sculpture has managed to reach

1.5 times its levels of 1990. The remaining three categories i.e. paintings, drawings

and prints only managed to reach their base year levels while remaining in very low

levels (half the prices of 1990) for the same period. One should also pay attention to

the fluctuations of the photography line in comparison to other fine arts. Photography

seems to be more sensitive to certain determinants than all the rest which appear

almost constant for the decade 1993-2003. From that point on there is a general

upward tendency which resulted to a peak in price levels in 2007. Perhaps this is

explained by the fact that this category is new to the auction market and therefore

buyers and sellers are more enthusiastic but also uncertain for long-term investments

on the medium.

Price indices for fine arts sold at auctions

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Graph 1. Data Source: Artprice.com

56

In order to test this a repeat sales study would be useful in the same manner

as similar studies have been for paintings. In the case of photographs however a

more detailed database is needed for such a research such as the Reitlinger

database for paintings.

The impact of the financial crisis on auction prices is apparent. All categories

show a downward slope in 2008. The time series stops in April 2009 and therefore is

not noted on the axis as a whole year, but surprisingly there is an upward tendency

just for photography in the beginning of 2009. Although the data is not yet sufficient

to show a clear positive tendency for the whole year, this positive sign, if verified,

brings up several questions yet to be answered with future data analyses.

In general we could say that all categories follow a similar pattern. Paintings

and drawings follow an almost identical path in the course of 20 years. Prints are the

ones that seem to be the most negatively affected always in comparison to the base

year of 1990 since in 2008 alone they reach the price levels of 2002 when the

apparent rise began. The same is the case for drawings and paintings; sculpture and

photography on the other hand have managed to stay in higher levels despite of the

sudden drop. As the financial crisis is still at large during the writing of this thesis,

there is no safe prediction as to where price levels can be at the next turning point.

4.3.2 Descriptive statistics

In order for the reader to have a better understanding of the data, first there

will be some comments on the frequency histograms on place of sale and auction

houses, which can be found in Appendix 1. This will help describe the characteristics

of the market for auctions in photography with the indication of market shares

concerning cases (number of artworks sold).

Then there will be comments on the diagrams of Appendix 2 showing the

tendencies of the average hammer prices and average prices per cm². By studying

those, the goal will be to identify possible patterns among the artists in the period of 8

years which is examined. If such patterns apply to the diagrams it would mean that

the price for auctioned photographs depends on factors which cannot be

overcome/influenced by the reputation of the artist. These factors could be a general

tendency of the art market to sell higher or lower depending on the market situation

of the specific period (bull / bear market). Increasing reputation, higher willingness to

pay from the demand side and the overall confidence on photography as an art

investment are all possible explanations for these phenomena.

57

The descriptive commentary will continue with Table 3 which summarizes the

whole dataset used in this study in matters of minimum/maximum price values per

artist, average prices and their standard deviations. This will be followed by a table of

the segmentation of prices (Showing the percentage of artworks at different price

levels). Afterwards these prices are summed into a chart to how the actual turnover

per auction house as a supplementary indication of market share as described in the

first section.

Photographer Frequency % Freq. Turnover % Turnover

Andreas Gursky 280 7.4 35,072,907 26.2

Bernd Hilla Becher 251 6.6 5,102,994 3.8

Candida Höfer 317 8.3 2,067,492 1.5

Cindy Sherman 798 21.0 28,107,240 21

Hiroshi Sugimoto 642 16.9 21,150,812 15.8

James Welling 65 1.7 201,730 0.2

Jeff Wall 43 1.1 3,842,187 2.9

Philip Lorca DiCorcia 163 4.3 1,973,313 1.5

Rineke Dijkstra 126 3.3 2,639,218 2

Thomas Demand 101 2.7 4,601,844 3.4

Thomas Ruff 630 16.6 12,571,644 9.4

Thomas Struth 386 10.2 16,789,623 12.5

Total 3802 100 134,121,004 100

Table 1: Frequencies of cases and turnover per artist

The above table shows the number of cases and the amount of turnover per

artist. The total of 3802 cases includes auctioned artworks of all 12 artists from 1989

until March 2009 respectively. This table is to show the fact that the number of cases

and the amount of turnover have no definite interrelation whatsoever. Gursky and

Becher have almost the same number of cases (280 to 251) but Gurksy has a

turnover of seven times the one of Becher. Ruff and Sugimoto have also

approximately the same number of cases with the latter having a bit less than double

the turnover of Ruff. Demand at another example shows more than double the

turnover than Höfer while having one third of total number of cases. Another fact that

makes a great impression is that for these 12 artists an auction sale course of 20

years (without everybody participating for all 20 years) there is a total turnover of 134

million euro. This amount is a good enough indicator of the total revenue that art

photography brings to auction houses. The annual revenues of the auction houses

for all kinds of fine art reached a dazzling amount of 9.2 billion dollars (approximately

58

7 billion €) in 2007, which is also considered the peak after 7 years of consecutive

price inflations on auction transactions.

Unfortunately there was no chance to acquire data

concerning all transactions on art photography for

auction houses, however with the use of the next

table (Table 2), which gives the amount of turnover

per year for all 12 artists, this comparison is more

plausible. In this case the artists of the sample sold

for a total of 26,039,598 € in 2007 which of course

compared to the total auction fine art revenue of 7

billion € it reaches approximately 0.5%. This comes

as no surprise when one considers the price levels of

Impressionist paintings like a Monet painting that sold

for approximately 22.3 million € in the same year118.

Another interesting assumption one can draw from

comparing Table 2 to the annual auction sales

turnover is the fact that they both follow a very similar

pattern. Starting from 1997 at almost 800,000€ there

is a gradual increase of turnover until 2000 to 2001

where amounts have reached 10 times those of 1997

at more than 9 million €, then there is a slight

downfall until 2003 ( 7.3 million €) and from 2004 until

2007 there is a significant increase that almost triples

the amount of turnover of 2003. In the grand scheme

of things the fine art auction sales showed a small

increase until 2000 then a fall and stagnation until

2003 and from 2004 until 2006 an increase which

resulted to doubling the 2003 amount ( around 2

billion € ) and reaching 7 billion in 2007

2008 is considered by Artprice a correction year and

this is probably also the reason why there is a

Table2: Turnover per year

*until March

118

Artprice (2008), 2007 Art market trends, Artprice.com.

Year N Sum

1989 2 7,184

1990 12 164,457

1991 22 188,711

1992 46 397,007

1993 46 451,301

1994 47 373,562

1995 42 312,786

1996 79 621,924

1997 61 794,658

1998 94 1,791,477

1999 119 3,051,065

2000 277 8,283,042

2001 283 9,282,327

2002 234 8,729,371

2003 260 7,389,148

2004 353 10,149,430

2005 399 12,952,843

2006 559 21,809,840

2007 436 26,039,598

2008 391 19,807,552

2009* 40 1,523,721

3802 134,121,004

59

downfall in the turnover of the sample artists. All in all it is plausible to say that there

is a general tendency for contemporary photography to follow the same pattern as

the entire fine art auction market. The number of cases also follows a similar course

as the turnover, something which was not the case with the artists. Again 2007 even

though it has fewer transactions than the previous year the total turnover rose by

approximately 4.2 million €. As the data for 2009 is at this point incomplete they are

not taken into account in the following graph (Graph 2) which depicts Table 2. By

comparing Graph 1 and Graph 2 it is easily noticeable that both lines follow

approximately the same pattern. Even though the first graph shows many

fluctuations, around 2000 and 2001 there is an increase with a slight decline over the

next two years and afterwards a steep rise with a peak in 2007 and the correction of

2008 leading to a drop. This also contributes to the fact that the sample chosen for

this study is a representative sample.

Graph 2

The following table (Table 3), summarises all basic information concerning

hammer prices of all artists. The N represents the number of total cases per artist i.e.

the number of hammered down artworks, which are considered sold artworks119.

119

Auction house theory (Aschenfelter, 1989 ) suggests that not all hammered objects are necessarily

sold. Because of lack of information for the fate of each particular artwork it is assumed that hammered

artworks are normally sold.

60

Photographer Categories N Minimum Maximum Mean Std. Deviation

Andreas Gursky Price sold in euros

280 1,982 2,277,000 125,260 245,022

Price per cm2 .268 571.83 7.08 34.28

Bernd Hilla Becher Price sold in euros

251 335 158,763 20,331 26,524

Price per cm2 .216 92.80 14.17 18.48

Candida Höfer Price sold in euros

317 237 78,400 6,522 9,753

Price per cm2 .162 17.09 1.60 1.55

Cindy Sherman Price sold in euros

798 357 1,364,930 35,222 78,068

Price per cm2 .038 658.87 17.88 44.52

Hiroshi Sugimoto Price sold in euros

642 1,024 1,217,370 32,945 83,184

Price per cm2 .323 69.58 7.04 5.84

James Welling Price sold in euros

65 246 12,940 3,104 2,737

Price per cm2 .187 31.43 3.29 5.52

Jeff Wall Price sold in euros

43 632 682,290 89,353 136,518

Price per cm2 .488 38.54 7.18 7.81

Philip Lorca DiCorcia

Price sold in euros 163

306 55,694 12,106 9,657

Price per cm2 .159 25.92 2.44 3.17

Rineke Dijkstra Price sold in euros

126 628 395,064 20,946 39,783

Price per cm2 .301 35.42 7.95 8.34

Thomas Demand Price sold in euros

101 633 174,130 45,563 42,705

Price per cm2 .255 30.70 2.90 3.58

Thomas Ruff Price sold in euros

630 255 111,929 19,955 24,279

Price per cm2 .025 28.27 2.05 2.49

Thomas Struth Price sold in euros

386 204 616,230 43,496 82,548

Price per cm2 .164 54.91 2.81 3.69

Table 3

The minimum and maximum figures show the area of hammer prices that

each artist has had until March 2009 starting from a specific year when their first

auction sale was recorded. These years start from approximately 1989 until 1999 and

are different for every artist and for that reason they are not included in the diagrams

which are presented in Appendix 2. These first years (until 1999) also include very

few cases for most artists in comparison to the eight-year period which is

represented in the mean price diagrams (See appendix 2) and therefore do not

influence the mean price and the standard deviation significantly. However, as the

reputation of the artists grows through time, the estimates of experts reach greater

levels and collectors become more and more interested in works of particular artists

61

like the ones of this sample, the need of a time scaled analysis of these figures

becomes more evident. In other words this table gives out the mean of means by

year and therefore does not reflect the true image of today. This can also be

observed when for instance the standard deviation of Andreas Gursky‟s artwork

prices (245,022) added to the mean (125,260) does not even reach half the level of

the maximum case. It does show however that the high 7-figure cases are quite few

in comparison to the total of 280 cases. These are called outliers in the statistics

language and can usually alter the statistical results. They are also the ones that

draw everyone‟s attention when they are published and form the highest records (like

the case of Gursky). With the exception of two artists (Wall, Welling) the number of

cases for each artist is high enough to allow the arithmetic mean to be more

trustworthy or to put it in a statistical manner, to describe the distribution of cases

better.

The year-assorted tables are shown in Appendix 3. In addition with the

diagrams they give out the tendency of the mean price per year for each artist. What

is interesting to observe is how a group of artists seems to follow a similar pattern

while others follow their own. For instance the period between 2003 to 2005 shows

quite low scores in the mean prices than for instance 2007. This applies to almost

everybody with a few exceptions. This is also one of the reasons why a group of 12

artists was chosen. This way we can infer with greater confidence that market trends

and other external factors do play a role in determining the price of auctioneered

items. Auction estimates are also based on the presumption that certain buyers

(collectors) show an inelastic demand for particular artists (or items). This of course

does not mean that buyers do not behave in an economic manner thus creating

these fluctuations in prices over time. The extent however of this influence is really

hard to measure and would probably need a demand side research to study the

behaviour of collectors, dealers and other buyers in a period of years with different

market possibilities (bull market, bear market). For instance the period of eight years

studied here does not take into consideration the financial crisis that started in 2008

but probably gives some hints of what is going to come next. A research study

including the financial crisis‟ years in the middle of the time series could quite

possibly present the impact (if there is any) on the auction sales. Already there is a

general tendency for a downfall. 2008 was according to Artprice120 a correction year

and they also confirm the above assumption that the auction market cannot help but

follow the financial crisis with lower turnovers in 2009 at least. At this point it is

120

Artprice 2009. Art Market Trends 2008.

62

difficult to make an estimate for the year‟s revenue with just three months of data

especially when important auctions take place in June and November. This remains

to be seen.

The following scatterplot (SC0) shows the spreading of all 3802 cases with

size in cm² on the horizontal axis and the price in euro on the vertical. From this

scatterplot we can see that the relation (regression line) between size and price is

generally positive but there are also outliers created by other factors (reputation of

artist, scarcity) that allow a small print of 13 by 18 cm or even less to fetch an

unusually high price. On the other hand there are cases of large prints (or

installations of 3 or more prints on one panel that make the total number of cm²

larger) that do not sell at a greater price than a single print.

SC0

With the exception however of few cases that can be counted manually using

this scatterplot, the vast majority of cases appears to show a low positive relation of

size and price at the below 500,000 euro area. It is also interesting to observe how

cases that belong to same size level (around 30,000cm²) are priced from a few

hundred Euros until approximately 750,000€.

This general view of scattered cases can be examined more thoroughly with

the use of the Scatterplots per artist which can be found in Appendix 4. These show

in general that some artists appear to be more size-dependent and therefore the

63

tendency line has different slopes (comparisons between artists should be made with

same scales of measurement on both axes like Struth and Wall for instance).

The table of segmentations gives an additional view on the general price

levels of the artists and how far the majority of artworks can be from the top hammer

prices. The general impression is that some artworks achieve large prices but they

appear to be only a 10% of the cases for each artist. A good example of this disparity

is Sugimoto where his top price has reached an amazing 7-digit number while 90% of

his artworks sold are below 38,000 €. The only one who shows the lowest disparity is

Demand with just seventy thousand € difference. He is also the youngest artist of the

sample which does not exclude the possibility that one of his works will achieve a

similar amount like the ones of Dijkstra, Struth or even more.

64

Segmentation of hammer prices by price segments

(EUR) 1997 – 2008* Gursky Becher Höfer Sherman Sugimoto DiCorcia Dijkstra Ruff Demand Struth

Top hammer price 2,277,000 158,763 78,400 1,364,930 1,217,370 55,694 395,064 126,038 174,130 616,230

90 % of hammer prices are under

262,752 40,797 14,820 106,315 38,376 23,699 39,170 55,215 107,341 122,137


178,636 22,107 8,525 55,125 27,668 18,544 25,523 36,117 85,052 61,928


114,305 11,500 6,144 38,205 22,001 13,608 19,500 23,229 62,130 30,168


66,737 7,947 4,500 25,439 18,400 11,297 14,370 15,068 49,569 15,172


42,310 4,747 3,456 15,084 14,871 8,953 12,189 4,643 41,591 8,659


21,855 2,552 2,600 6,000 12,579 7,700 9,360 2,400 27,808 5,803


11,248 1,329 1,900 3,033 9,655 6,588 6,489 1,800 2,863 4,387


7,872 700 1,125 1,718 6,677 5,327 4,602 1,380 1,600 3,466


4,953 300 767 1,022 3,254 3,754 1,919 1,001 1,000 2,050

Table 4 * Unfortunately this information was not available for all 12 artists, therefore Wall and Welling are not included in this table. Artprice.com 2009

65

The following pie charts show the percentage and total turnover figures per

auction house and per place of sale for all cases. The total number of auction houses

which were analysed from the data is 70. The frequency table of all cases per auction

house (Appendix 1) however, showed the enormous difference in market share

between the major three auction houses (Christie‟s, Sotheby‟s and Phillips de Pury &

Company) and the rest. This gives one aspect on the situation and the turnover chart

was necessary to verify this. The depiction of all 70 auction houses in a single chart

was not considered useful and for this reason the remaining 67 auction houses were

clustered together (shown with green colour in the chart) and they are showing a

surprising 2.64% of total turnover for all 12 artists. The number of artists of the

sample and the fact that they are internationally known leads us to the conclusion

that this is most probably the market share status quo we would encounter, if all

photographers whose work is sold through auction houses were included in the data.

In other words there is statistically enough information to infer that the market of

auctioned photographs is basically a triopoly consisting of three major sellers that

share a total of almost 97% of the total market, and a large number of smaller sellers

who share the rest.

Pie chart on turnover percentage per auction house for all 3802 cases.

66

This situation shows the necessity of investigating its possible effects on the

sale prices and this is mainly the reason why the auction houses were chosen as a

separate variable to be tested as an indicator of price. Czusack121 derives from her

findings that Sotheby‟s is more successful than Christie‟s in attracting vendors when

it comes to Picasso paintings. In the case of photographers however this is not true

as Christie‟s gathers almost 50% more revenue than Sotheby‟s and 45% of the total

sample, according to the chart. There is no evidence on why this is so, and the

reasoning behind it could be quite multi faceted (tradition, networks, etc) but can also

be by chance. In any case the reasoning behind these market shares requires more

a more specialised approach with appropriate data.

It is also important to notice the number of cases and the amount of total

revenue that is gathered in different places were auctions are held. New York and

London alone gather a striking percentage of 97% with other traditional art auction

places for paintings for instance like Paris to be crowded with the remaining 36 cities

around the globe in a 2.97% of total revenue. In the frequency of cases table for all

cities (Appendix 1) Cologne comes in third place (206 transactions) but the turnover

percentage is below 2 – 3% of the total. The number of cases can be explained from

the fact that 7 out of 12 artists are Europeans, with 6 being from Germany and also

living and working in Germany. The major source of auction revenue however,

appears to be located “west of Berlin”. This cluster of transactions and apparently

important transactions of hundreds of thousands of € is not by chance. For this

reason the place of sale will be tested as a separate hypothesis in the analysis

chapter. What is also interesting to note is the fact that these two categories are not

irrelevant from one another. The major auction houses are traditionally located in

London and New York even though they have departments in many other cities.

Christie‟s for instance has further salerooms in Amsterdam, Dubai, Geneva, Hong

Kong, Milan, Paris and Zurich, while Sotheby‟s is also found in Doha (Middle East),

Sydney and Melbourne. However the two major cities in combination with the three

large auction houses seem to have the lion‟s share in the photography auction

market. What strikes as surprising is the apparent absence of Paris among the cities

with the most revenue. Paris seems to be active in photography with the organising

of the Paris-Photo an event that has taken place every year for 12 years now

(Artprice.com, 2009) and attracts many collectors of vintage and modern

photographs from all over the world. However the participation of auction houses in

121


229-247.

67

this event like Artcurial, Piasa and Ader which set up thematic sales, doesn‟t seem to

influence the overall market share of Paris in the auction market, at least not to the

contemporary part.

Pie chart on turnover percentage per place of sale for all 3802 cases.

By this point the reader should have a clear image of the photography auction

market depending on the descriptive data of this chapter. In the methodology chapter

the further analysis and the variables chosen will be presented and in the analysis

chapter the results of the study.

68

5. Methodology

5.1 Introduction

In this chapter the methodology of the research is discussed. The sections

following deal with the variables and hypotheses generated, the data collection and

the statistical methods used to draw conclusions.

5.2 Variables

The variables chosen for this study derive from the auction sale database of

Artprice.com. The data was chosen by artist name and then transferred to SPSS.

The variables consist of most information that could be derived from these databases

and are coded as follows:

5.2.1 Size

The size of the artwork plays a very important role in all visual arts in

determining the price. By the use of several artists I will try to show the amount of this

impact on the price for the whole sample of twelve artists and for each one

separately. The height and width are multiplied to produce the variable used in cm².

In rare cases of artworks which are transparencies in light-boxes the third dimension

is not taken into consideration and the artwork is treated as a two-dimensional print.

5.2.2 Type of print

This variable states the different types of print as described in the database. A

basic distinction is between colour prints and black&white which are given as

“Gelatine silver prints”. The colour prints are divided into the following categories: C-

print stands for Chromogenic print and it is the most commonly used type of colour

paper for photographs. Cibachrome is a less common type of material which was

used to print directly from colour transparecies (slides) and was much more

expensive. Today the vast majority of conventional printing has been substituted with

digital printers which use either inks or chemicals. These are mostly encountered for

artworks printed around the year 2000 and beyond. These prints, when specified, are

marked as “Digital print”.

69

5.2.3 Year of artwork

The year of artwork states the year when the photo was originally taken. This

does not mean however that the print was made the same year. In some cases there

were two dates available with the second indicating when the image was printed or

reprinted at the certain size. Due to lack of adequate information for most cases

these reproduction dates have been omitted and the original shooting date is used.

For this reason “vintage” (old prints) are treated as contemporary prints in this study.

5.2.4 Year of sale

The year of sale indicates the year when the artworks were hammered down.

This variable is not used as an independent variable but to group the cases in a time

series and make the comparison between years possible.

5.2.5 Place of sale

This variable is used in descriptive statistics to show which city (and country)

are the most popular places for selling art photographs in auctions. The distinction

between city and country in two separate variables was not considered necessary as

the city is probably the one that plays the most significant role for an auction and not

the country in general.

5.2.6 Auction house

Another variable used primarily in descriptive statistics to state the popularity

of certain auction houses over others which in this market comes to no surprises. It is

interesting of course to check this frequency of cases for each artist separately and

make comparisons.

5.2.7 Price

The prices are the dependent variable in this analysis but are also presented

with descriptive statistics to show the disparity of values the mean prices and their

standard deviations per artist and year. For this reason the price per cm² was also

computed to check its tendency in a period of eight years. All prices acquired from

70

Artprice.com have been adjusted with a base year in 1997 so that it allows users to

objectively compare the trends in the market value of different artists.

5.3 Hypotheses

The goal of my thesis is twofold. The first one is to describe a part of the

secondary market on art photography using auction sale data. The second one is to

specify the amount of impact of several determinants on the hammer price of a

contemporary photographic artwork. From this second section several hypotheses

can be formulated.

The artists are evaluated by experts and according to the criticism attributed to their

work the estimates that auctioneers make are higher. By testing prices in this

database it means that these estimates have been accepted by the buyers as the

price levels of the artists.

H1: Each artist (reputation) affects the price of the artwork.

The effect of artwork size in the hammer price implies that the larger the size of the

artwork the higher the hammer price at an auction must be. This is generally

accepted by literature for all visual art objects and especially for painting122

H2: The size of the artwork increases the price of the artwork (controlling for

the reputation of the artist)

The effect of the artwork year in the price implies that the older the photograph the

higher value it has accumulated (in a cultural sense) and this is reflected on its price.

In the case of contemporary photography this is an open question and this analysis

will attempt to shed some light on it within the restrictions of the data available.

H3: The age of the artwork increases the price of the artwork. (controlling for

the reputation of the artist)

The type of print may influence the estimates and therefore the price of the

photograph. This is examined at the same manner as in painting where oil canvas is

priced differently than acrylic etc.

122


Edward Elgar, 57-68.

71

H4: The type of print will influence the price of the artwork. (controlling for the

reputation of the artist)

The place of sale is a factor that could also influence the price perhaps indirectly.

Certain cities seem to attract the most important buyers because important auctions

are traditionally held in these particular cities.

H5: The place of sale affects the price of the artwork. (controlling for the


The auction house and its reputation is another positive influence for a higher

hammer price. It will be interesting to check this relation in comparison or

combination with H4. This derives from the descriptives where the obvious oligopoly

of three major auction houses dominates the entire market. This leads to a certain

reputation of the three that could have a (positive) influence on the price.

H6: The auction house affects the price of the artwork. (Controlling for the


5.3.1 Hypotheses testing

The analysis will go on to evaluate the impact of several predictors

(independent variables) on the price (dependent variable) as stated in the

hypotheses above. For this reason a number of linear regressions will be performed

to check the impact of every predictor on the price. The number of cases for each

artist (with two exceptions of Wall and Welling) is adequate (over 100 cases) to

perform such regressions123. In order to be able to measure the effects of each artist,

without assuming that all have the same amount of influence on the price, 11 dummy

variables were computed for every artist with Andreas Gursky being the reference

category. In the same manner all variables that stemmed from nominal data were

turned into dummy variables. The “type of print” variable was turned into 3 dummy

variables labelled “cibachrome”, “gelatine silver print” and “digital” with “C-print” being

the reference category. The “Place of sale” variable initially included 39 different

cities across the globe but it was considered helpful to cluster 36 of those into one

and use it as a reference category in comparison to “London” and “New York”. In the

same way the dummy variables for “Auction house” were computed. Out of 70

auction houses mentioned in the data, 67 of those were clustered together into the

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Field, A. 2005. Discovering Statistics using SPSS. London, UK. Sage Publications.

72

reference category and are compared to “Christie‟s”, “Phillips De Pury & Company”

and “Sotheby‟s”.

The regression model is the linear regression model and the equation of the

general research question for this study can be written as:

Price = ß0 + Σ ßi*(artisti) + ßj*(size) + ßk*(age of artwork) + Σ ßl*(type of printl)

+ Σ ßm*(place of salem) + Σ ßn*(auction housen)

ß0 = the coefficient of the reference category (Andreas Gursky is the ref.

category in this analysis).

ßi = the coefficient of each of the 11 remaining artists.

ßj = the coefficient of the size variable

ßk = the coefficient of the age of artwork (Year of Sale – Year of artwork)

ßl = the coefficient of each of the three types of print (cibachrome, gelatine

silver and digital)

ßm = the coefficient of each of the two places of sale (London, New York)

ßn = the coefficient of each of the three major auction houses (Christies,

Phillips, Sothebys)

In the testing of hypotheses where the coefficients of dummy variables are computed

(ßl , ßm , ßn ) the reference category is Andreas Gursky in combination with the

relative reference categories (C-print, Other auction houses and Other places of

sale).

From this general model each individual linear regression model containing

dummy variables is used. For instance to test the first hypothesis for artists the above

equation can be written Price = ß0 + ß1*(artist2) + ß2*(artist3) + ß3*(artist4) +

ß4*(artist5) + ß5*(artist6) + … + ß11*(artist12). At this point the multiple regression

analysis starts bringing each of the other predictors (as dummies) in comparison to

the dummy variables for artists. The second regression analysis is performed with

size which is numeric and therefore does not need to be transformed. However size

itself does not play the same role for each artist. In order to show this difference a

number of interaction dummies were computed by multiplying each artist (dummy)

with the size variable. From these computations 11 interaction artist-size dummy

variables were generated and entered into the regression analysis. In the third

regression analysis checking for the “age of artwork” hypothesis the first regression

includes the numeric variable ”age of artwork”, which is computed by deducting the

“Year of artwork” from the “Year of sale”. Likewise, in the second step of the process

11 interaction artist-age of artwork dummies were created and added in the

independent variable list. In the test of the fourth hypothesis concerning the type of

73

print, along with the three dummy variables computed for the main effects, 33

interaction variables were computed (11 artist dummies x 3 type of print dummies). In

some cases there are artists who do not use certain materials. For instance Jeff Wall

does not use digital methods and therefore the interaction dummy variable “wall-

digital” is zero. These are therefore excluded from the coefficients tables. The same

principle applies to all other similar cases. In the fifth hypothesis the variable “place of

sale” is tested and therefore 22 additional interaction variables are computed (11

artist dummies x 2 place of sale dummies) or in other words the interaction of every

artist with London and New York. Finally at the sixth hypothesis 33 additional

dummies were computed showing the interaction of every artist and the three auction

house dummies (Christie‟s, Phillips, Sotheby‟s).

The following chapter presents the analysis and its results as well as a

general conclusion of the results of this thesis.

74

6. Analysis

6.1 Introduction

The analysis will continue with the regression analyses results testing every

hypothesis stated in the methodology chapter. The presentation of the regression

tables will include those from the main effects results and the ones from the

interactions due to their bulkiness will be commented following afterwards. Each

table shows the coefficients of each regression model and is therefore named

coefficient table (CT).

6.2 Model results

CT 1

Model 1:

Artists

Unstandardized Coefficients Standardized

Coefficients

t Sig.

B Std. Error Beta

1 (Constant) 125,260.382 5,354.165 23.395 .000

dummy becher -104,929.729 7,787.574 -.278 -13.474 .000

dummy Höfer -118,738.325 7,347.664 -.350 -16.160 .000

dummy sherman -90,038.277 6,222.999 -.391 -14.469 .000

dummy sugimoto -92,315.192 6,416.375 -.368 -14.387 .000

dummy welling -122,156.844 12,335.156 -.169 -9.903 .000

dummy wall -35,907.196 14,674.348 -.040 -2.447 .014

dummy diCorcia -113,154.167 8,826.730 -.244 -12.819 .000

dummy Dijkstra -104,314.208 9,611.021 -.199 -10.854 .000

dummy demand -79,697.570 10,399.048 -.137 -7.664 .000

dummy ruff -105,305.392 6,434.906 -.417 -16.365 .000

dummy struth -81,763.950 7,032.913 -.263 -11.626 .000

a. Dependent Variable: Price sold in euros

R Square: 0.092

The first regression model resulted in a rather poor fit of R Square (0.1)

indicating that only a 10% of the variance could be explained with these predictors. In

other words the very low and high prices of artworks could be explained to a limited

extent by the reputation of artists alone. This is realistic when we take into

consideration that practically all artists have auction sales of a few hundred or

thousand € and the price can reach up to 7-figure numbers for some. The

significance of the result however was encouraging as they were not a matter of

75

chance (Sig. = 0.000). In the coefficients table shown above (CT1) it is interesting to

observe how – on average – the price level of the reference category (Gursky) is

higher than all the rest. In order to compute the price of any other artist his B score

has to be deducted (added negative score) to the B0 (Constant). Wall therefore

comes in second place and Demand, Struth and Sherman follow.

CT 2

Model 2:

Artists & Size


Coefficients

t Sig.

B Std. Error Beta

1 (Constant) 50,985.026 5,463.040 9.333 .000

dummy becher -40,454.910 7,372.375 -.107 -5.487 .000

dummy Höfer -61,699.384 6,918.233 -.182 -8.918 .000

dummy sherman -34,406.577 5,934.762 -.149 -5.797 .000

dummy sugimoto -31,509.525 6,157.955 -.126 -5.117 .000

dummy welling -55,689.288 11,372.549 -.077 -4.897 .000

dummy wall -5,791.594 13,296.199 -.007 -.436 .663

dummy diCorcia -59,189.186 8,184.477 -.128 -7.232 .000

dummy Dijkstra -50,483.411 8,875.219 -.096 -5.688 .000

dummy demand -66,015.369 9,405.756 -.113 -7.019 .000

dummy ruff -80,726.149 5,873.500 -.320 -13.744 .000

dummy struth -54,077.098 6,423.424 -.174 -8.419 .000

Size in cm2 3.050 .104 .460 29.245 .000


R Square: 0.259

This table shows therefore the price levels of each artist much better than a

simple mean could do. What is also important to notice is the strong significance

levels for every artist dummy <0.01. The general assumption from this regression is

that the artist reputation alone does seem to be able to predict a higher price to a

certain extent. Therefore there is sufficient statistical evidence to verify the first

hypothesis. From the descriptive statistics however we know that the variation of

prices within the artists is quite large. In order to explain this remaining variance,

additional predictor variables were added to the following analyses. Next regression

includes the “size” variable.

The size variable when added into the regression analysis allows for 26% of

the variance to be explained thus making the model fit much better. The analysis of

variance of the whole model also shows a strong significance. In the second table

76

(CT2) the relation between Gursky and the rest remains almost the same with Wall

being a little less expensive but his significance level drops greatly allowing the

reader to infer that this coefficient is more a product of chance. The significance

remains strong for every other artist. The coefficient of the size variable shows that

on average a unit (cm²) increase in the size of the photograph rises the price with 3 €

for each artist.

However the size might not have the same effect to every artist and therefore

a series of interaction variables was calculated as mentioned in the methodology

part. The table generated is not shown but is definitely worth commenting on. The fit

of the model becomes even better reaching an R square value of 0.357 thus

explaining 36% of the variance. The coefficients now show that some size interaction

dummies effects are almost negated by the original size variable coefficient (Becher,

DiCorcia). They certainly do not reach the 3.050 coefficient of the main effects table

above. Sugimoto and Wall seem not to be influenced (their negative coefficients are

quite low) but their significance levels are not adequate to verify that relation. In this

regression the reference category is Gursky together with size (as a size interaction

dummy variable). From these regressions we can assume that size plays an

important role to defining a price but its level of effectiveness is not the same for

every artist separately. Statistical evidence though is adequate to infer that there is a

positive relation between artwork size and artwork price as stated in the second

hypothesis.

The analysis continues with testing the main effects of the “age of artwork”

variable. The age of artwork in the main effect analysis did not meet the

expectations. The model fit was poor explaining a mere 9% of the variance. In the

coefficients table the relations of the artist dummies remained similar towards the

reference category but the new entered variable turned out to be insignificant. These

results would generally leave out this variable and consider the hypothesis as not

valid. However the computation of the interaction dummy variables of each artist with

the age of the artwork gave out very interesting results. The R Square was increased

to almost 14%. The significance levels for every dummy variable were 0.000

including the original “age” variable. The effect of age of artwork was practically

negated for some artists (Sugimoto, Welling, DiCorcia, Dijkstra) while for others it

even showed a positive relation (Wall, Demand, Sherman) and for Becher, Höfer,

Struth and Ruff the relation was slightly negative.

77

CT 3

Model 3:

Artists & Age of

artwork


Coefficients

t Sig.

B Std. Error Beta

1 (Constant) 127,515.945 5,923.302 21.528 .000

dummy becher -100,607.642 8,636.221 -.257 -11.649 .000

dummy Höfer -118,849.681 7,559.479 -.343 -15.722 .000

dummy sherman -87,267.281 6,543.246 -.373 -13.337 .000

dummy sugimoto -90,902.991 6,564.014 -.359 -13.849 .000

dummy welling -120,815.425 12,793.496 -.163 -9.444 .000

dummy wall -33,055.985 15,373.356 -.036 -2.150 .032

dummy diCorcia -112,688.918 8,963.320 -.244 -12.572 .000

dummy Dijkstra -104,160.072 9,789.719 -.198 -10.640 .000

dummy demand -79,471.597 10,674.468 -.135 -7.445 .000

dummy ruff -104,451.264 6,591.109 -.406 -15.847 .000

dummy struth -80,692.629 7,166.596 -.259 -11.260 .000

years between sale

and creation

-249.574 223.750 -.021 -1.115 .265


R Square: 0.091

For Becher who is the oldest artist, even a generation older, the age of

artworks seems to work surprisingly negatively for his prices. This means in other

words that more recent artworks are the ones that sell more in comparison to older

prints. For Gursky it also has a somewhat strong negative relation. This has probably

to do with the different styles that he shows between the 80s and the 90s.

Photographs from the 90s are mostly the ones he is better known for, and some of

which have also reached the record levels of price which he holds like the “99 Cent”

(1999). As a result there is no sufficient statistical evidence to infer that age of

artwork and price follow a positive relation.

The analysis goes on to add the “type of print” dummy variables. The

reference category in this case is Gursky and C-print. The fit of the model is again in

the 10% area but still significant. The table of coefficients below (CT4) shows

different relations between the artist dummies and the reference category. The

significance levels for two of the three newly added variables are low meaning that

any effect they can have is mostly occurred by chance. Only the cibachrome variable

shows a significant positive relation to an increase in price. This is probably due to

the material itself, which is less common and more expensive to buy. Generally we

78

could say that artists who have sold cibachrome prints have been associated with

larger sale prices, especially in Gursky‟s and Wall‟s cases. For Wall the cibachrome

coefficient counteracts with the artist‟s coefficient which is negative in relation to the

reference category. The second regression concerning the type of print included 33

new dummy variables, each artist interacting with every type. However not all artists

work with all materials and therefore some of those interaction dummies were

automatically left out (scored zero). The R square was raised a little and reached

12% while certain interaction variables turned out insignificant. The certain analysis

did not achieve anything better than the main effects regression. From this analysis

no statistical evidence showed a definite connection between type of print and price

with perhaps the exception of colour prints from the main effects regression model.

CT 4

Model 4:

Artists & Type of

print


Coefficients

t Sig.

B Std. Error Beta

1 (Constant) 117,686.558 5,466.167 21.530 .000

dummy becher -89,658.621 9,431.184 -.237 -9.507 .000

dummy Höfer -113,277.074 7,361.715 -.334 -15.387 .000

dummy sherman -82,151.468 6,397.143 -.356 -12.842 .000

dummy sugimoto -76,996.905 8,399.959 -.307 -9.166 .000

dummy welling -110,912.784 12,633.096 -.153 -8.780 .000

dummy wall -48,883.418 14,809.510 -.055 -3.301 .001

dummy diCorcia -107,060.696 8,832.508 -.231 -12.121 .000

dummy Dijkstra -99,693.141 9,588.415 -.190 -10.397 .000

dummy demand -73,512.693 10,803.900 -.126 -6.804 .000

dummy ruff -99,358.286 6,489.108 -.394 -15.312 .000

dummy struth -76,680.059 7,234.747 -.247 -10.599 .000

dummy cibachrome 37,204.750 6,061.214 .101 6.138 .000

dummy gelatinsilver -7,744.463 5,494.489 -.039 -1.409 .159

dummy digital -9,567.917 9,837.115 -.016 -.973 .331


R Square: 0.102

In this case the hypothesis should be partly rejected, meaning that it is true for

certain types but it is not true for every type of print. In order for this to be tested

more thoroughly perhaps a larger sample with more details on the type of print would

be necessary. According to the present data, colour prints have a slight positive

79

influence but all other types are possibly obscured by other factors determining the

price.

Next hypothesis to be tested is the place of sale. In this regression model the

reference category is once more Gursky with the sum of small auction houses. The R

square remained in the same levels as with the previous models. The significance

levels for all coefficients however were quite high with nearly all being zero. The

London and New York dummy variables were also significant and also showed a

positive relation which was expected. It was also rational to see the coefficients of the

artists becoming negative meaning that artists and small auction houses show lower

prices on average. Then the interaction variables came into the regression analysis

which surprisingly turned all artist dummies insignificant (even the reference

category) making heir coefficient values unreliable.

CT 5

Model 5:

Artists & place of

sale


Coefficients

t Sig.

B Std. Error Beta

1 (Constant) 97,586.126 6,.228.653 15.667 .000

dummy becher -99,762.831 7,742.053 -.264 -12.886 .000

dummy Höfer -109,630.549 7,358.085 -.323 -14.899 .000

dummy sherman -87,787.736 6,215.670 -.381 -14.124 .000

dummy sugimoto -94,304.711 6,367.165 -.376 -14.811 .000

dummy welling -120,075.019 12,244.887 -.166 -9.806 .000

dummy wall -36,938.464 14,545.160 -.042 -2.540 .011

dummy diCorcia -115,096.621 8,776.303 -.248 -13.114 .000

dummy Dijkstra -102,622.609 9,533.262 -.196 -10.765 .000

dummy demand -80,418.933 10,303.787 -.138 -7.805 .000

dummy ruff -100,846.808 6,397.052 -.399 -15.765 .000

dummy struth -81,680.243 6,971.240 -.263 -11.717 .000

dummy london 36,599.747 4,658.562 .160 7.856 .000

dummy new york 29,942.761 3,935.667 .157 7.608 .000


R Square: 0.109

The fit of the model was again increased by 2 percentage points to reach

12%. In the interaction regression the coefficients of the place dummies are both

significant and contribute positively to the dependent variable, however the

interaction dummy coefficients lowers this effect for most artists but it still remains

80

positive. It is interesting to notice that London has a higher coefficient score than

New York even though the descriptives show a lion‟s share for number of cases and

turnover for New York. This is probably explained by the fact that some of the most

expensive artworks, the two most expensive to be exact, were sold in London. New

York of course is not far behind. Wall is the only artist who appears insignificant in

the table. This analysis shows that the place of sale and especially the two major

cities play a positive role in the increase of prices, therefore the fifth hypothesis

should be considered to be valid.

Finally to test the auction house hypothesis the dummies of the three major

auction houses were tested in comparison to the reference category of Gursky and

the sum of the remaining auction houses. A similar situation as with the place of sale

can be described for the auction houses as well (CT6 below).

CT 6

Model 6:

Artists & auction

house


Coefficients

t Sig.

B Std. Error Beta

1 (Constant) 97,782.749 6,315.828 15.482 .000

dummy becher -101,275.828 7,738.633 -.268 -13.087 .000

dummy Höfer -109,569.142 7,375.485 -.323 -14.856 .000

dummy sherman -88,894.254 6,176.579 -.386 -14.392 .000

dummy sugimoto -95,451.180 6,376.626 -.381 -14.969 .000

dummy welling -120,943.403 12,236.063 -.167 -9.884 .000

dummy wall -36,979.159 14,549.573 -.042 -2.542 .011

dummy diCorcia -114,740.456 8,792.011 -.248 -13.051 .000

dummy Dijkstra -105,690.616 9,531.622 -.202 -11.088 .000

dummy demand -79,808.411 10,311.047 -.137 -7.740 .000

dummy ruff -100,831.768 6,404.616 -.399 -15.744 .000

dummy struth -82,200.284 6,973.695 -.264 -11.787 .000

dummy christies 34,079.812 4,385.371 .174 7.771 .000

dummy phillips 25,661.010 4,772.293 .111 5.377 .000

dummy sothebys 33,273.155 4,570.053 .156 7.281 .000


R Square: 0.108

Again a model fit of 11% is shown and the significance levels are identical

with the ones of CT5. The same goes for the standardised betas indicating that place

of sale and auction houses cases are dispersed in a similar way and are therefore

81

affecting the artist coefficients. In this case however the coefficients of the auction

house dummies reflect the market shares of each auction house. Christie‟s shows a

value of 34,079 with Sotheby‟s following closely with 33,273 and Phillips in the third

place with 25,661. With the addition of the interaction dummy variables the effects

are identical with those of the previous analysis with Jeff Wall appearing insignificant

with all three auction houses meaning that any effect on prices of his artworks

caused by the reputation of the auction is probably by chance. The coefficients for

the auction houses show differences from the actual turnover percentages shown

from descriptive statistics. Sotheby‟s appears to have a more positive influence,

Phillips comes in second and Christie‟s shows the lowest coefficient among the

three, always in comparison to the reference category of Gursky and the sum of

smaller auction houses. From this analysis derives enough statistical evidence to

infer that the large auction houses influence the prices of artists positively.

6.3 Conclusion

The field of photography has indeed much to show judging from the

descriptive statistics presented in this thesis. It is a fairly new market which has

grown significantly during the past two decades with prices having risen to almost

double their levels of 1990. Even though the total turnover share of photography is

quite low in comparison to the one of paintings, this constant increase shows that

investors are starting to show more interest to other categories of artworks apart from

paintings and drawings. This interest is also shown by the breaking of records for the

highest price ever charged for a photograph like in 2007. 2008 was a correction year

and 2009 may be even worse for the art market but as in all sectors of the economy

there are always inflations and deflations of prices.

The above analysis presented a number of linear regressions and their

findings. The artists were tested separately and then in combination with every other

predictor. Additional regressions were carried out; one of those was a model with all

main effect variables which showed nothing more than the individual regressions. As

Buelens and Ginsburgh124 stated in their study, the analysis of the whole dataset may

lead to significant results but with low R² s, meaning that a low percent of the

variance is explained by the model. This was also the case in this thesis. The goal

was to identify the impact of these predictors on the price of artworks in combination

with the reputation of artists which is a function of several variables by itself. The

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Buelens, N. , Ginsbergh, V. 1993. Revisiting Baumol’s Art as a floating Crap Game. American

Economic Review vol:37 1351-1371.

82

estimates which are given by experts play also a significant role in establishing the

price levels and they can only be verified when the artwork is sold. In this study the

reputation of artists was included in the artist variables. The tests of several other

predictors led to the conclusion that size plays the most important role in explaining

the great variance in prices something which is also found in painting auctions as is

the case with all studies concerning125. If the outliers of the dataset are excluded

then this relation will probably be much stronger. This of course does not mean that

the other predictors are not important in determining the price of an artwork. As The

place of sale and the auction house variables showed that they influence prices to an

extent, especially in particular combinations showing that certain artists benefit more

from some auction houses than others. This however could be because of the artists‟

own reputation. Even if reputation acts as a potential bias in this case I still believe it

didn‟t affect the impact of the auction houses partly because there is an overlap in

that impact. The results of the age of the artwork test were also interesting to see.

Older artworks from the same artist are not connected with higher prices at least for

contemporary art. Buyers are apparently more interested in newer artworks a fact

that could have many interpretations. It could have artistic reasons (subject,

aesthetics) or purely technical (re-print on archival paper that ensures larger image

life expectancy). This brings forward the variable “type of print”. Unfortunately the

data collected presented some inconsistencies in describing the material used in

adequate detail. There were different descriptions used from each auction house that

indicated the same type with alternative terms or the descriptions were too general.

The fact however, that several types are mentioned means that the type of print is

being taken into account when estimating prices. For this reason it would be

advisable for all auction houses, galleries and other organisations active on art

photography market to use a standard terminology, which can be provided by the

International Standards Organisation (ISO).

6.3.1 Future research

The present thesis cannot describe the entire art photography market but it

gives however some initial thoughts on how to look upon the art market and the

different forms of art it deals with. For artists this is perhaps clear but for economists

visual arts are usually taken as a whole or painting is used as a prime example,

implying that all other elements comprising the visual art sector are either

125


modern and contemporary art. Journal of cultural Economics vol:30 73-84.

83

insignificant or they follow the exact same pattern as painting. This was a detail that I

also wanted to confirm or reject by making this thesis. The result was that all

categories follow the same pattern approximately but they differ in fluctuations

between years. There was however no surprising case of one category showing a

steep increase while others being on a downward slope. Of course in order to make

a detailed comparison, data concerning both photographers and painters should be

gathered following certain criteria (genres of artists, size of artworks etc) that was not

possible in the given time frame. It could of course be the starting point of a future

research. Another interesting aspect is the identification of the percentage of art

photography in the primary market, in galleries for instance, in comparison with new

paintings or also a demand study for art photography entering the market.

Also a repeat sales regression would also be interesting to explain more of

this apparent booming of the art photography auction market during these two

decades, provided that enough cases can be gathered from the total number of

photographers whose works are sold at auction. Finally another very interesting

research would be to make a comparison between contemporary and old masters of

photography (Stieglitz, Steichen, etc) who also sell for high prices.

A final question which could not be covered by the data of this thesis is a

general demand study for photographs; who buys photographic artworks, which

country or region shows the greatest interest in the photographic medium; is there a

preference in contemporary or old master work and by which kinds of buyers;

answers to such questions may well contribute to the explanation of the price

function for photographs.

84

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Sites consulted www.artprice.com www.christies.com www.phillipsdepury.com www.sothebys.com

www.rkd.nl

http://www.artprice.com/

http://www.christies.com/

http://www.phillipsdepury.com/

http://www.sothebys.com/

http://www.rkd.nl/

87

Appendix 1

Bar Charts

Place of sale frequency per artist

BC1.1 Cases time period: 1994-2009(March)

88



89



90


BC7.1 Cases time period: 1993-2008

91



92



93


Auction house frequency per artist


94



95



96


BC7.2 Cases time period: 1993-2008

97



98




99

Cases time period: 1989-2009(March)

100

Cases time period: 1989-2009(March)

101

Appendix 2

Diagrams

AHP Andreas Gursky

63.531 81.766

113.817

68.022 92.724

114.482

207.466

161.123

339.570

-

50.000

100.000

150.000

200.000

250.000

300.000

350.000

400.000

2000 2001 2002 2003 2004 2005 2006 2007 2008

Year

Av.

Pric

e

D1.1

APcm² Andreas Gursky

65

3,7

25,6

5,8 5,5 5,8 5,5

8,5

0

5

10

15

20

25

30

2000 2001 2002 2003 2004 2005 2006 2007 2008

Year

Av.

Price

per cm

²

D1.2

AHP stands for Average Hammer Price per year of transactions.

APcm² stands for Average Price per cm² per year of transactions.

102

All prices are in euros with base year 1997

AHP Bernd Hilla Becher

16.378

45.752

25.303

17.758

31.554

15.939

27.892

19.765 22.874

-

5.000

10.000

15.000

20.000

25.000

30.000

35.000

40.000

45.000

50.000

2000 2001 2002 2003 2004 2005 2006 2007 2008

Year

Av.

Pric

e

D2.1

APcm² Bernd Hilla Becher

13,3

26,9

18,717

22,6

12,8

17,1 16,3

13,2

0

5

10

15

20

25

30

2000 2001 2002 2003 2004 2005 2006 2007 2008

Year

Av.

Price

per cm

²

D2.2




103

AHP Candida Hoefer

2.510

3.563 3.659 4.433

6.260

7.351 6.976

8.483 8.792

-

1.000

2.000

3.000

4.000

5.000

6.000

7.000

8.000

9.000

10.000

2000 2001 2002 2003 2004 2005 2006 2007 2008

Year

Av.

price

D3.1

APcm² Candida Hoefer

2,4

1,7 1,8

1,3 1,21,4 1,5

2,2

1,5

0

0,5

1

1,5

2

2,5

3

2000 2001 2002 2003 2004 2005 2006 2007 2008

Year

Av.

Price

per cm

²

D3.2




104

AHP Cindy Sherman

36.587 46.653

31.803 40.482

33.401 28.075 31.643

98.482

61.610

-

20.000

40.000

60.000

80.000

100.000

120.000

2000 2001 2002 2003 2004 2005 2006 2007 2008

Year

Av.

Pric

e

D4.1

APcm² Cindy Sherman

12,3

34,7

12,6

23,3

19,4

12,9

16,9

36,7

28,6

0

5

10

15

20

25

30

35

40

2000 2001 2002 2003 2004 2005 2006 2007 2008

Year

Av.

Price

per cm

²

D4.2




105

AHP Hiroshi Sugimoto

9.665 13.877 14.851 14.456

20.101

36.806 39.252

62.631

49.264

-

10.000

20.000

30.000

40.000

50.000

60.000

70.000

2000 2001 2002 2003 2004 2005 2006 2007 2008

Year

Av.

Pric

e

D5.1

APcm² Hiroshi Sugimoto

3,6

5,4 5,55 5,2

9,2 9,29,8

7,1

0

2

4

6

8

10

12

2000 2001 2002 2003 2004 2005 2006 2007 2008

Year

Av.

Price

per cm

²

D5.2




106

AHP James Welling

1.784

2.762

-

1.878

6.034

4.727

2.555 2.971 3.059

-

1.000

2.000

3.000

4.000

5.000

6.000

7.000

2000 2001 2002 2003 2004 2005 2006 2007 2008

Year

Av.

Pric

e

D6.1

APcm² James Weling

13,2

3,6

0

8,9

2,3

0,8

3,13,9

2,5

0

2

4

6

8

10

12

14

2000 2001 2002 2003 2004 2005 2006 2007 2008

Year

Av.

Price

per cm

²

D6.2




107

AHP Jeff Wall

290.072

64.498 77.050

96.188

66.597

127.453

53.320 55.950

223.575

-

50.000

100.000

150.000

200.000

250.000

300.000

350.000

2000 2001 2002 2003 2004 2005 2006 2007 2008

Year

Av.

Pric

e

D7.1

APcm² Jeff Wall

7,1

3,8

5,4

8,9

4,9

8

6,2

16,1

11,9

0

2

4

6

8

10

12

14

16

18

2000 2001 2002 2003 2004 2005 2006 2007 2008

Year

Av.

Price

per cm

²

D7.2




108

AHP Philip Lorca DiCorcia

15.383

17.336

9.486

6.666

10.385

14.899 14.705

9.308 9.453

-

2.000

4.000

6.000

8.000

10.000

12.000

14.000

16.000

18.000

20.000

2000 2001 2002 2003 2004 2005 2006 2007 2008

Year

Av.

Pric

e

D8.1

APcm² Philip Lorca DiCorcia

3,6

4,5

2,1

1,41,2

4,5

2,5

1,51,2

0

0,5

1

1,5

2

2,5

3

3,5

4

4,5

5

2000 2001 2002 2003 2004 2005 2006 2007 2008

Year

Av.

Price

per cm

²

D8.2




109

AHP Rineke Dijkstra

20.321 18.301

52.949

29.222

14.717 9.914

16.010 11.465 10.679

-

10.000

20.000

30.000

40.000

50.000

60.000

2000 2001 2002 2003 2004 2005 2006 2007 2008

Year

Av.

Price

D9.1

APcm² Rineke Dijkstra

10,2

11,5

6,6

11,5

9,5

5,96,8 6,7

1,10

2

4

6

8

10

12

14

2000 2001 2002 2003 2004 2005 2006 2007 2008

Year

Av.

Price

per cm

²

D9.2




110

AHP Thomas Demand

31.048

61.683

35.883

55.409

47.364

22.462

51.996

61.475

24.473

-

10.000

20.000

30.000

40.000

50.000

60.000

70.000

2000 2001 2002 2003 2004 2005 2006 2007 2008

Year

Av.

Pric

e

D10.1

APcm² Thomas Demand

3,4

1,6

2,5

0,9

1,9

3,7

2,5

4,9

2,8

0

1

2

3

4

5

6

2000 2001 2002 2003 2004 2005 2006 2007 2008

Year

Av.

Price

per cm

²

D10.2




111

AHP Thomas Ruff

9.781

19.700 19.948 20.560 20.802

23.782 22.962

30.597

20.320

-

5.000

10.000

15.000

20.000

25.000

30.000

35.000

2000 2001 2002 2003 2004 2005 2006 2007 2008

Year

Av.

price

D11.1

APcm² Thomas Ruff

1,1

1,8

1,5

1,9

1,4

2,4

2,8 2,9

2,6

0

0,5

1

1,5

2

2,5

3

3,5

2000 2001 2002 2003 2004 2005 2006 2007 2008

Year

Av.

Price

per cm

²

D11.2




112

AHP Thomas Struth

59.866

26.083

73.291

36.119 30.354

25.080 32.196

97.606

62.981

-

20.000

40.000

60.000

80.000

100.000

120.000

2000 2001 2002 2003 2004 2005 2006 2007 2008

Year

Av.

Price

D12.1

APcm² Thomas Struth

3,12,8 2,8

3,1

1,9

2,6

3,73,5

3,2

0

0,5

1

1,5

2

2,5

3

3,5

4

2000 2001 2002 2003 2004 2005 2006 2007 2008

Year

Av.

Price

per cm

²

D12.2




113

AHP

-

50.000

100.000

150.000

200.000

250.000

300.000

350.000

400.000

2000 2001 2002 2003 2004 2005 2006 2007 2008

Year

Av.

Pric

e

Gursky

Becher

Hoefer

Sherman

Sugimoto

Wall

Welling

DiCorcia

Dijkstra

Struth

Ruff

Demand

D0.1

114

APcm²

0

5

10

15

20

25

30

35

40

2000 2001 2002 2003 2004 2005 2006 2007 2008

Year

Av.

Price

per cm

²

Gursky

Becher

Hoefer

Sherman

Sugimoto

Wall

Welling

DiCorcia

Dijkstra

Struth

Ruff

Demand

D0.2

115

Appendix 3

Tables

T1

Becher N Minimum Maximum Mean Std. Deviation

2000 Price sold in euros 11 3,170 78,269 16,378 21,161

Price per cm2 2.19 47.15 13.34 12.74


Price per cm2 .55 75.13 26.93 28.12


Price per cm2 1.52 61.07 18.67 19.89


Price per cm2 1.40 57.25 16.98 18.20

2004 Price sold in euros 25 498 126,060 31,554 33,427

Price per cm2 .56 92.80 22.62 25.16


Price per cm2 1.49 74.61 12.77 17.49


Price per cm2 1.01 60.21 17.08 19.00


Price per cm2 2.45 66.47 16.32 16.91


Price per cm2 .22 75.29 13.17 18.63

T2

Gursky N Minimum Maximum Mean Std. Deviation


Price per cm2 0.91 8.80 3.73 2.03


Price per cm2 1.22 17.75 5.95 3.33


Price per cm2 1.06 10.19 5.03 2.69


Price per cm2 0.99 8.11 3.68 1.91


Price per cm2 0.73 571.83 26.64 113.62


Price per cm2 0.78 50.16 5.85 8.61

2006 Price sold in euros 39 4,200 1,716,000 204,070 362,074

Price per cm2 0.82 24.46 5.76 5.66


Price per cm2 1.07 32.41 5.75 7.26


Price per cm2 1.00 34.24 8.48 8.82

116

Höfer N Minimum Maximum Mean Std. Deviation

2000 Price sold 16 237 13,562 2,510 3,619

Price per cm2 .19 17.09 2.44 4.28

2001 Price sold 18 511 10,811 3,563 3,236

Price per cm2 .37 4.50 1.73 1.17

2002 Price sold 21 700 13,297 3,659 3,487

Price per cm2 .50 2.64 1.76 .56

2003 Price sold 21 305 11,315 4,433 2,944

Price per cm2 .22 2.87 1.26 .80

2004 Price sold 30 562 27,461 6,260 6,877

Price per cm2 .37 2.55 1.16 .55

2005 Price sold 42 355 46,695 7,351 8,949

Price per cm2 .32 4.51 1.43 .90

2006 Price sold 69 251 59,820 6,976 9,966

Price per cm2 .25 8.26 1.50 1.18

2007 Price sold 46 339 78,400 8,483 13,742

Price per cm2 .32 14.68 2.17 2.29

2008 Price sold 44 450 55,440 8,792 13,145

Price per cm2 .16 4.60 1.55 .77

T3

Sherman N Minimum Maximum Mean Std. Deviation

2000 Price sold 70 454 267,954 36,587 54,307

Price per cm2 .32 182.01 12.34 27.52

2001 Price sold 36 818 336,022 46,653 66,535

Price per cm2 .04 658.87 34.72 110.35

2002 Price sold 24 600 131,688 31,803 42,844

Price per cm2 .19 95.79 12.56 20.85

2003 Price sold 37 600 152,023 40,482 42,409

Price per cm2 .41 290.81 23.27 51.87

2004 Price sold 58 681 323,862 33,401 54,718

Price per cm2 .33 122.63 19.39 29.31

2005 Price sold 71 495 282,414 28,075 55,528

Price per cm2 .13 242.38 12.92 33.16

2006 Price sold 69 500 452,400 31,643 63,034

Price per cm2 .18 178.57 16.86 33.91

2007 Price sold 64 475 1,364,930 98,482 195,655

Price per cm2 .13 348.58 36.68 72.52

2008 Price sold 64 432 525,375 61,610 108,713

Price per cm2 .19 407.00 28.60 66.55

T4

117

Sugimoto N Minimum Maximum Mean Std. Deviation

2000 Price sold 37 2,189 21,244 9,665 4,321

Price per cm2 1.59 8.55 3.62 1.59

2001 Price sold 41 4,293 45,924 13,877 9,131

Price per cm2 1.64 15.70 5.44 3.44

2002 Price sold 49 5,000 38,784 14,851 7,094

Price per cm2 2.05 11.19 5.49 2.25

2003 Price sold 44 4,710 46,272 14,456 8,412

Price per cm2 2.19 11.95 4.98 2.50

2004 Price sold 62 3,097 139,572 20,101 21,980

Price per cm2 1.00 10.76 5.24 2.14

2005 Price sold 62 1,351 551,850 36,806 71,400

Price per cm2 .60 53.22 9.20 8.06

2006 Price sold 118 1,024 489,786 39,252 66,264

Price per cm2 .32 46.93 9.21 6.01

2007 Price sold 102 1,104 1,217,370 62,631 151,924

Price per cm2 1.01 69.58 9.79 7.96

2008 Price sold 81 1,122 682,560 49,264 112,632

Price per cm2 .51 29.61 7.07 5.28

T5

Welling

N Minimum Maximum Mean Std. Deviation

2000 Price sold 1 1,784 1,784 1,784 .

Price per cm2 13.21 13.21 13.21 .

2001 Price sold 3 546 6,649 2,762 3,377

Price per cm2 1.55 5.84 3.56 2.16

2002 Price sold 0

Price per cm2

2003 Price sold 2 1,584 2,171 1,878 415

Price per cm2 2.23 15.48 8.86 9.36

2004 Price sold 5 410 11,615 6,034 5,348

Price per cm2 1.01 3.75 2.27 1.36

2005 Price sold 4 2,863 8,659 4,727 2,693

Price per cm2 .38 1.30 .82 .41

2006 Price sold 12 246 5,820 2,555 1,563

Price per cm2 .32 23.32 3.11 6.56

2007 Price sold 5 914 5,636 2,971 1,876

Price per cm2 .65 12.10 3.86 4.71

2008 Price sold 12 286 12,940 3,059 3,455

Price per cm2 .48 9.47 2.50 3.08

T6

118

Wall N Minimum Maximum Mean Std. Deviation

2000 Price sold 1 290,072 290,072 290,072 .

Price per cm2 7.08 7.08 7.08 .

2001 Price sold 6 632 145,610 64,498 56,840

Price per cm2 .49 7.05 3.84 2.52

2002 Price sold 1 77,050 77,050 77,050 .

Price per cm2 5.39 5.39 5.39 .

2003 Price sold 3 52,122 156,366 96,188 53,957

Price per cm2 2.30 18.68 8.86 8.66

2004 Price sold 4 1,092 177,353 66,597 83,659

Price per cm2 2.35 10.02 4.92 3.45

2005 Price sold 2 3,272 251,634 127,453 175,618

Price per cm2 4.68 11.27 7.98 4.66

2006 Price sold 5 1,733 152,406 53,320 61,863

Price per cm2 3.72 10.65 6.21 3.04

2007 Price sold 5 3,571 114,960 55,950 49,806

Price per cm2 7.54 30.06 16.11 10.47

2008 Price sold 6 1,700 682,290 223,575 308,740

Price per cm2 1.28 38.54 11.92 14.12

T7

DiCorcia N Minimum Maximum Mean Std. Deviation

2000 Price sold 10 2,611 55,694 15,383 16,081

Price per cm2 .87 17.90 3.55 5.13

2001 Price sold 15 4,797 31,362 17,336 7,195

Price per cm2 1.46 10.81 4.53 2.80

2002 Price sold 13 2,246 18,418 9,486 6,290

Price per cm2 .88 5.67 2.14 1.37

2003 Price sold 21 601 16,332 6,666 3,395

Price per cm2 .19 5.27 1.41 1.10

2004 Price sold 14 3,651 18,610 10,385 5,247

Price per cm2 .47 2.13 1.19 .40

2005 Price sold 20 3,000 42,961 14,899 13,474

Price per cm2 .51 25.92 4.47 6.45

2006 Price sold 31 657 47,490 14,705 10,816

Price per cm2 .16 12.49 2.54 2.40

2007 Price sold 19 306 28,180 9,308 5,874

Price per cm2 .54 3.90 1.45 .77

2008 Price sold 15 3,264 18,874 9,453 4,238

Price per cm2 .58 2.11 1.25 .42

T8

119

Dijkstra N Minimum Maximum Mean Std. Deviation

2000 Price sold 13 3,981 102,105 20,321 25,432

Price per cm2 .84 22.49 10.22 6.93

2001 Price sold 15 1,452 102,062 18,301 26,209

Price per cm2 1.39 35.42 11.47 11.44

2002 Price sold 16 2,500 395,064 52,949 95,409

Price per cm2 1.63 13.33 6.61 4.00

2003 Price sold 12 1,633 138,992 29,222 37,447

Price per cm2 .64 35.25 11.46 12.03

2004 Price sold 9 4,000 29,617 14,717 8,829

Price per cm2 .91 22.85 9.53 8.36

2005 Price sold 14 1,700 21,111 9,914 5,467

Price per cm2 .46 17.66 5.90 5.91

2006 Price sold 21 903 48,000 16,010 12,953

Price per cm2 .30 26.67 6.79 8.48

2007 Price sold 14 670 23,251 11,465 8,066

Price per cm2 .56 21.28 6.69 7.20

2008 Price sold 9 628 32,016 10,679 9,708

Price per cm2 .44 2.09 1.10 .58

T9

Demand N Minimum Maximum Mean Std. Deviation

2000 Price sold 2 16,457 32,488 24,473 11,336

Price per cm2 1.20 5.54 3.37 3.07

2001 Price sold 8 39,975 95,206 61,475 19,094

Price per cm2 1.00 3.16 1.58 .78

2002 Price sold 11 8,933 131,688 51,966 36,375

Price per cm2 1.34 4.60 2.53 .94

2003 Price sold 5 750 79,530 22,462 34,363

Price per cm2 .25 1.52 .89 .45

2004 Price sold 6 1,079 75,933 47,364 26,074

Price per cm2 .99 3.52 1.89 1.03

2005 Price sold 15 1,771 116,550 55,409 43,091

Price per cm2 .44 14.36 3.69 3.92

2006 Price sold 26 633 174,130 35,833 50,203

Price per cm2 .30 7.05 2.48 1.75

2007 Price sold 16 1,357 129,166 61,683 42,739

Price per cm2 1.28 30.70 4.95 7.06

2008 Price sold 11 767 160,000 31,048 48,548

Price per cm2 .66 11.26 2.78 2.96

T10

120

Ruff N Minimum Maximum Mean Std. Deviation

2000 Price sold 53 716 83,859 9,781 16,226

Price per cm2 .31 4.61 1.12 .82

2001 Price sold 58 762 111,929 19,700 30,498

Price per cm2 .24 6.89 1.84 1.49

2002 Price sold 37 700 81,444 19,948 24,822

Price per cm2 .20 9.94 1.47 1.70

2003 Price sold 33 1,102 62,546 20,560 19,897

Price per cm2 .11 8.62 1.87 1.51

2004 Price sold 71 374 78,979 20,802 19,060

Price per cm2 .22 7.66 1.45 1.13

2005 Price sold 69 700 97,638 23,782 21,516

Price per cm2 .23 18.92 2.42 3.03

2006 Price sold 88 720 101,751 22,962 27,334

Price per cm2 .22 15.74 2.85 2.64

2007 Price sold 74 1,086 106,183 30,597 31,050

Price per cm2 .46 14.39 2.87 2.38

2008 Price sold 74 255 91,840 20,320 25,395

Price per cm2 .15 12.70 2.57 2.54

T11

Struth N Minimum Maximum Mean Std. Deviation

2000 Price sold 37 1,841 266,604 59,866 73,429

Price per cm2 .78 8.58 3.06 1.78

2001 Price sold 35 1,125 170,103 26,083 43,055

Price per cm2 .72 6.27 2.77 1.43

2002 Price sold 25 1,800 307,272 73,291 88,780

Price per cm2 .70 6.91 2.77 1.75

2003 Price sold 34 500 316,206 36,119 63,096

Price per cm2 .92 11.87 3.07 2.59

2004 Price sold 44 2,162 177,009 30,354 38,997

Price per cm2 .56 10.44 1.94 1.72

2005 Price sold 48 800 207,228 25,080 40,776

Price per cm2 .50 10.10 2.59 2.00

2006 Price sold 59 493 340,929 32,196 65,360

Price per cm2 .36 54.91 3.74 7.21

2007 Price sold 39 1,057 616,230 97,606 155,782

Price per cm2 .56 14.04 3.51 3.55

2008 Price sold 31 948 491,720 62,981 124,991

Price per cm2 .39 23.28 3.18 4.89

T12

121

Appendix 4

Scatterplots

SC1 SC2

SC3 SC4

SC5 SC6

122

SC7 SC8

SC9 SC10

SC11 SC12

Art Photography Prices - Erasmus University … Stournaras Master...Art Photography Prices In the...

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